Analysis Of Fintech Startups In India-dissertation.docx

Analysis of Fintech Startups in India Influence Processes for Fintech Services Breakout: An Elaboration Likelihood Model

A Project Report Submitted in Partial Fulfilment of the Requirement for the Award of Degree of

MASTER OF BUSINESS ADMINSTRATION - FULL TIME

Under the Guidance of

DR. PANKAJ SINHA

Submitted By:

SAURABH SINGH ROLL NO: F-365 MBA (FT) 2017-19 AREA CODE: FIN

Faculty of Management Studies University of Delhi Delhi-110007

January 2019 1

CERTIFICATE This is to certify that the project titled “Analysis of Fintech Startups in India- Influence Processes for Fintech Services Breakout: An Elaboration Likelihood Model” submitted in the partial fulfilment of the requirements for the degree of Master of Business Administration is a record of original research work carried out by myself. Any material borrowed or referred to is duly acknowledged.

Saurabh Singh Roll No. F-365 MBA FT 2017-19 Faculty of Management Studies

University of Delhi

This is to certify that the aforementioned project titled “Analysis of Fintech Startups in IndiaInfluence Processes for Fintech Services Breakout: An Elaboration Likelihood Model” submitted by Saurabh Singh, MBA (FT), Batch of 2019, Roll No. F-365 has been carried out under my supervision.

Dr. Pankaj Sinha Project Guide Faculty of Management Studies

University of Delhi 2

ACKNOWLEDGEMENT First and foremost, I would to like to thank Dr. Pankaj Sinha for giving me an opportunity to work under her in an area of mutual interest. I express my deepest gratitude for her able guidance. Her continued supervision and encouragement has made it possible for me to complete this dissertation. I also would like to thank all the individuals who spared their precious time to share with me their views and suggestions pertaining to my dissertation. I would also like to thank all the faculty members and staff of FMS Delhi for their continuous support and help. Most importantly I would like to thank the Dean, FMS for facilitating a smooth process for pursuing the dissertation. Finally, I am indebted to my family for their love, patience, and support during the entire time of my studies.

Saurabh Singh

MBA (FT) 2017-19 Roll No. F-365 FMS Delhi

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Table of Contents LIST OF ABBREVIATIONS ....................................................................................................................... 6 EXECUTIVE SUMMARY .......................................................................................................................... 7 Research Objective .................................................................................................................................... 7 INTRODUCTION ........................................................................................................................................ 8 FinTech...................................................................................................................................................... 8 Fintech in India........................................................................................................................................ 10 Indian FinTech segments......................................................................................................................... 11 Need Gap for Fintech in India ................................................................................................................. 13 Credit gap in the MSE segment ........................................................................................................... 14 FinTech Investments ............................................................................................................................... 15 Global Funding in FinTech.................................................................................................................. 15 Indian FinTech Growth Drivers .............................................................................................................. 15 LITERATURE REVIEW ........................................................................................................................... 18 Prior Research ......................................................................................................................................... 18 Elaboration-likelihood model (ELM) ...................................................................................................... 18 LASIC ..................................................................................................................................................... 21 HYPOTHESIS DEVELOPMENT.............................................................................................................. 23 Factors Analysis ...................................................................................................................................... 26 RESEARCH MODEL ................................................................................................................................ 28 Research Design ...................................................................................................................................... 28 Results of Path Analysis-SPSS Model .................................................................................................... 31 INSIGHTS .................................................................................................................................................. 33 Breakout FinTech Segments ................................................................................................................... 33 Indian Fintech Breakout-Framework Design .......................................................................................... 34 Fintech Adoption-Other Customer Insights ............................................................................................ 35 IDENTIFYING BREAKOUT SEGMENTS IN FINTECH ....................................................................... 37 Alternate Lending .................................................................................................................................... 37 Evaluation using Proposed Framework ............................................................................................... 37 Peer-to-Peer Lending ........................................................................................................................... 37 Peer-to-Peer Lending-Results from Consumer Survey........................................................................ 38 Peer-to-Peer Lending-Results from Stakeholder Interviews ............................................................... 39 Payments ................................................................................................................................................. 40 Evaluation using Proposed Framework ............................................................................................... 40

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M-Wallets and PPIs ............................................................................................................................. 40 Merchant Payments and PoS Services ................................................................................................. 42 Cross border payments ........................................................................................................................ 42 Investment Management ......................................................................................................................... 43 Evaluation using Proposed Framework ............................................................................................... 43 Online Financial Advisors ................................................................................................................... 43 Roboadvisory ....................................................................................................................................... 43 Discount Brokers ................................................................................................................................. 44 Banktech .................................................................................................................................................. 44 Evaluation using Proposed Framework ............................................................................................... 44 Customer Onboarding.......................................................................................................................... 44 Blockchain ........................................................................................................................................... 45 Big Data, AI and Robotics ................................................................................................................... 45 InsurTech................................................................................................................................................. 46 Evaluation using Proposed Framework ............................................................................................... 46 Insurance aggregators .......................................................................................................................... 46 IOT and Wearables .............................................................................................................................. 47 Personal Finance Management ................................................................................................................ 48 Evaluation using Proposed Framework ............................................................................................... 48 CONCLUSION ........................................................................................................................................... 49 REFERENCES ........................................................................................................................................... 50

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LIST OF ABBREVIATIONS PE GP LP LBO M&A MBO MBO DCF FCFF WACC EV EBITDA E P IS BS CF DS RA LTM EBT SG&A R&D CAGR NFDP WCN CAPEX NOPLAT IPO LIBOR NWC PPE GAAP EMEA APAC ROA ROE

Private Equity General Partners Limited Partners Leveraged Buyout Mergers & Acquisitions Management Buyout Management Buy-in Discounted Cash Flow Free Cash Flow to Firm Weighted Average Cost of Capital Enterprise Value Earnings before Interest, Taxes, Depreciation and Amortization Earnings Price Income Statement Balance Sheet Cash Flow Statement Debt Schedule Return Analysis Last twelve Months Earnings before Taxes Selling, general and administrative Research & Development Compounded Annual Growth Rate Net Financial Debt Position Working Capital Needs Capital Expenditures Net Operating Profit Less Adjusted Taxes Initial Public Offering London Interbank offered Rate Net Working capital Property, plant and equipment Generally Accepted Accounting Principle IRR Internal Europe, Middle East and Africa Asia and Pacific area countries Return on assets Return on equity

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EXECUTIVE SUMMARY Fintech has been attracting significant investment in VC capital over the recent times but, there's a relative lack of studies on the factors induce the acceptance or denial of Fintech services. For example, there's no analysis that explains what data points measure best in influencing user perceptions and why, whether or not such influence applies equally or differentially across user populations, and whether or not these influence effects are temporally persistent.

Research Objective 1. Provide a picture of India’s Fintech business 2. Establish the key drivers for nursing an enabling Fintech scheme in the country. RQ1. What influence processes form user acceptance of latest Fintech and how? RQ2 Do the consequences of those influence processes vary across a user population, and how? RQ3. How ¿persistent are the consequences of those influence processes over time? 3. Benchmark the current state of these drivers in India across Fintech segments. 4. Develop Framework for Fintech Service Segments Adoption in India. Identify Key Breakout Fintech Segments for the Indian Market. 5. Share a roadmap for strengthening the drivers and developing a Fintech ecosystem in India. Understanding the dynamics of acceptance-related influence processes is important for theoretical as well as practical reasons. This paper examined the acceptance of Fintech services through a combination of supply side and demand side drivers. The objective of this research is to identify the factors that compel users to accept Fintech services. In order to achieve this goal, this study aimed to develop a model on Fintech service acceptance by utilizing the Elaboration Likelihood Model (ELM) proposed by Petty and Cacioppo [1] and selecting variables of the Technical Acceptance Model (TAM) proposed by Davis [2] and several other variables. In addition, it adopted Concern for Information Privacy (CFIP), an increasingly aggravating problem in India’s financial industry, and self–efficacy as moderating variables to examine their 7

impact on intention to use. At Supply side we have looked at the LASIC principle which defines five important attributes of business models that can successfully harness financial technology to achieve the objective of creating a sustainable social business for financial inclusion. The five attributes are: low margin, asset light, scalable, innovative and compliance easy. Understanding the dynamics of acceptance-related influence processes is very important for theoretical in addition to practical reasons. This paper looks at the acceptance of Fintech services through a mix of supply and demand side drivers. The target of this analysis is to spot the factors that compel users to just accept Fintech services so as to attain this goal, this study aimed to develop a model on Fintech service acceptance by utilizing the Elaboration likelihood Model (ELM) proposed by Petty and Cacioppo [1] and choosing variables of the Technical Acceptance Model (TAM) projected by Davis[2] and several other variables. Additionally, it adopted Concern for information Privacy (CFIP), an exasperating drawback in India’s monetary business, and self–efficacy as moderating variables to look at their impact on intention to use. In order to assess the breakout potential, as well as the timing of breakout, this paper has developed a customized FinTech breakout assessment framework for the Indian FinTech market, drawing from the learnings of the study. “

INTRODUCTION FinTech The term “FinTech,” that is that the short variety of the phrase finance technology, denotes corporations or representatives of corporations that mix money services with fashionable, innovative technologies. Fintech may be a service sector that uses mobile-centered IT technology to reinforce the potency of the national financial economy. As a term, it's a compound of “finance” and “technology”, and conjointly refers to industrial changes achieved from the convergence of monetary services and IT. It's an innovative service that provides differentiated money services through new technologies, like mobile, social media, and IoT (Internet of Things). A recent example is the mobile-based payment and settlement system, that is that the most representative service of its

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kind in India. In terms of trade, it refers to the development wherever a non-financial business uses innovative technology to produce services, like remittal, payment and settlement, and investment, while not operating with a financial company. Major examples include PayTM and PhonePay. In addition to giving services within the banking sector, there also are FinTechs that distribute insurance and alternative monetary instruments or offer third party services. In an generous sense of the term, “FinTech” encompasses corporations that merely offer the technology (such as software system solutions) to financial service suppliers.

Figure: Fintech Services

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Fintech in India FinTech is one in every of the quickest rising areas in banking and financial services. It's creating the expertise of banking and finance a lot of intuitive, customized and empowering. The convergence of economic services and exponential technologies are going to be key to make a robust digital economy, and lead India’s transformation. Armed with new knowledge and analytics capabilities, plus light-weight platform and nearly zero process prices, FinTechs services are complementing and in some cases questioning the standard banking and money services establishments globally. FinTechs hope to achieve the aim for providing cashless digital payments services. On the disposal facet, low penetration of retail and MSME credit alongside the promise of higher expertise and quicker turnaround have created sturdy propositions for patrons. Fintechs in most of the opposite segments as well as Investment Management, Personal Finance Management, BankTech and InsurTech have initiated the market creating method and presently target specific market niches.

Figure: Popular areas of Funding in FinTech(US $m)

Technology has been a key enabler within the growth of a digital economy. Over the years, Indian banks and monetary services suppliers have bit by bit adopted technology to boost reach, client service and operational effectiveness with evolving market and technological advances. However, the pace of technology adoption has not been in proportion to with it's potential and 10

therefore there are gaps within the penetration of monetary services. For instance, there's a credit demand offer gap within the small and little Enterprise (MSE) section significantly for small enterprises

Indian FinTech segments In the Indian context, FinTech can be broadly aligned across the following twenty segments, across six broad financial services areas. Among these segments, Digital Payments are at the forefront of leading India’s FinTech sector. Correspondingly, digital payments have conjointly garnered the lion’s share of VC funding as compared to alternative segments. Post the Government's demonetization initiative the growth in digital payments is exclusive, as payments stay associate degree innovation cluster wherever penetration is very low and there are still areas of friction that new FinTech players will right to supply price. The retail disposition phase, wherever there's a convergence to the regulated regime as most of the FinTech players during this space, as well as P2P lenders, various Credit marking platforms and Crowd Sourcing platforms, aer eventually being brought into the restrictive ambit. The MSME disposition space is witnessing new FinTech players addressing the structural problems with data spatiality and reducing turnaround times for underwriting loans to tiny businesses. Expectedly, the plus facet of the banking business remains a white area wherever there are restricted innovations, with the exception of Peer-to-peer disposition platforms.” Areas

FinTech Segments

Credit

“01. Peer-to-Peer Lending

Brief Description

•” All forms of lending market places including Peer02. Crowd Funding to-Peer lenders and market 03. Market Place for Loans places that connect borrowers 04. Online Lenders – on-book with both, institutional and lenders; lending by NBFCs 05. Credit Scoring Platforms”

• Also includes crowd funding and equity funding platforms”

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• NBFCs that use alternative scoring and digital channels for acquisition” Payments

06. “M-wallets and PPIs 07. Merchant Payments and PoS Services 08. International Remittance 09. Crypto Currencies”

• “Services that enable transfer of funds for various use cases - P2P (Person-toPerson), P2M (Person-toMerchant), G2P (Government-to-Person) etc. • Services targeted at both Payees and Merchants by enabling requisite payment infrastructure through mobile or other technologies”

Investment Management

10. “Robo Advisors 11. Discount Brokers 12. Online Financial Advisors”

Personal Finance Management

13. “Tax Filling and Processing 14. Spend Management and Financial Planning 15. Credit Scoring Services”

Bank tech

16. “ Big Data 17. Blockchain 18. Customer Onboarding Platforms”

“Wealth advisory services delivered through technology governed rules and investment strategies” • “Tools and services for active management of individual financial profiles (e.g. spend, investments, credit profile, etc.) “ “Services that utilize many data points such as financial transactions, spending patterns to build the risk profile of the customer. This provides an alternate to traditional underwriting methods that are unable to serve people with limited credit data. • There is significant value in unstructured data. However, it is difficult to derive value from unstructured data, owing to challenges in analyzing it. A number of new tools are being developed to derive value from large data sets.” 12

InsurTech

19.” Insurance Aggregator

“Small business insurance

20. IOT, Wearables and Kinematics”

• Usage based insurance”

Need Gap for Fintech in India

Figure: Finance Infrastructure in India

Traditional Banks and associated financial establishments have viewed technology as an enabler to business propositions, instead of making new business propositions themselves. Financial Technology (FinTech) corporations however area ever-changing that role by leverage digital technologies to form new business propositions and target new market segments that up to now weren't reaching their potential. FinTech within the truest sense is that the application of technology to supply new Financial product associated services to new market segments in an economically viable manner. From a business model perspective, the FinTech sector is marked by technology corporations that either shall disintermediate, or partner with incumbent Banks and monetary establishments looking on strategic narrative and market landscape. Hence, FinTech is progressively turning into a vital focus space for all the key stakeholders in India’s monetary Services trade – Regulators, ancient

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Banks, NBFCs, Payment Banks, Investors, Payment Service suppliers, Broking and Wealth Management corporations, Insurance suppliers and pureplay FinTech players. Credit gap in the MSE segment Revenue No. of

Credit

Bank Credit

Credit

Demand(INR

Supply(INR 000

Gap(INR

000 crore)

crore)

000 crore)

414

92

322

15 - 30 Lakh 5.6

168

62

106

30 lakh - 1.5 4.5

477

203

274

234

103

131

720

357

363

2013

817

1196

Segment(INR) Units(Mn)

<15 Lakh 41.4

Crore 1.5 Crore - 3 1.3 Crore 3 Crore - 18 1.8 Crore Total 54.6

Note: Credit Demand is calculated based on revenue using appropriate multipliers

BANK HARDWARE PROVIDERSS

NBFC SOFTWARE PROVIDERS

PAYMENTS

FINANCE

FinTech TECHNOLOGY CLOUD PROVIDERS

WEALTH MANAGEMENT PLATFORM PROVIDERS

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FinTech Investments Global Funding in FinTech

≥ $500m

≥ $100m

≥ $10m

< $10m

Indian FinTech Growth Drivers India remains one of the largest markets where the structural enablers to setup and incubate FinTech companies have come together strongly. 01. Combination of steady economic process with low penetration of monetary services: India’s GDP value is anticipated to grow at 6-8% for ensuing decade, therefore driving financial gain and consumption levels of households still as businesses. including low penetration of home credit in tier two and three cities, mortgage, investment and plus management services, the banking and money services market is probably going to grow at 2-2.5 times of real value

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growth, therefore sustaining each incumbents and new FinTech entrants. Further, improvement in digital infrastructure (E.g. net and smartphone penetration) outside urban and tube centres can drive adoption of digital money services 02. Giant public sector banks and insurers insulating market growth: Public sector banks and insurance companies’ are step by step however incessantly losing market share to non-public banks and insurers severally, because of their inability to outgrow the market. However this steady loss, Public sector banks still account for seventieth market share of deposits and credit. Going forward, new personal sector banks, together with new differentiated banks area unit doubtless to be the beneficiaries of rising market opportunities. In conjunction with the differentiated banks, rising FinTech players within the areas of payments, loaning and investment management also will have the benefit of low penetration and target niche areas.

Figure: VC-Backed FinTech Deals

Figure: Number of Fintech Companies Launched

03. Regulative forbearance toward FinTech: Indian regulative authorities together and in association with RBI and SEBI have adopted an accommodative stance toward a rising FinTech sector, while not forcing in preventative tips to overregulate the world. Despite catching up with the speedily evolving eco system, Indian regulators have adopted an informative approach and are proactively foreseeing the necessity for adequate rules, particularly within the areas regarding public funds i.e. peer-to-peer disposition, crowd sourcing and various currencies. 16

04. India Stack and Net knowledge proliferation to enhance Financial services utility infrastructure and property to support digital money services: India Stack may be a set of Application Programming Interfaces (APIs) that permits FinTech corporations, developers and governments to utilize India’s distinctive digital Infrastructure towards presence-less, paperless, and cashless money service delivery though Bharat stack, hopped-up by Jan Dhan, Aadhaar & Mobile trinity, will modify incumbent banks and money service suppliers, however its true power is controlled by FinTech corporations in considerably reducing prices of acquisition. UPI is often considered a game changer, because it has mass attractiveness, attributable to its universal acceptance and safety features. Aadhaar, that currently extends to ~1.1 Bn Indians are often levied for effective identity verification of economic transactions. It’s proving to be associate degree optimum digital identity, and it offers users the flexibility to firmly utilize their life science, once enterprise money transactions

India Stack

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LITERATURE REVIEW Prior Research Prior analysis on individual Fintech acceptance has been familiar by the dominant theoretical views. The primary perspective, focused on the Theory of reasoned action [6] and also the theory of planned behavior[7], has centered on individual perceptions because the primary drivers of acceptance intention and behavior. IT-specific variants of those theories embrace the technology acceptance model[5] , the Decomposed theory of planned behavior[8], and also the unified theory of acceptance and use of technology [4]. Collectively, these theories counsel that users' IT acceptance intention and behavior square measure formed by salient user cognitions associated with the target IT like its perceived quality and simple use, users' view toward IT acceptance, social norms associated with acceptance, and conditions sanctionative or restrictive acceptance[4]

Elaboration-likelihood model (ELM) One theoretical perspective that may facilitate inform our standing of influence processes in IT acceptance is that the elaboration-likelihood model (ELM). The ELM classifies influence mechanisms or routes into central and peripheral varieties supported the sort of knowledge processed by a given user (e.g., task-relevant arguments or easy cues), explains circumstances that that user is also additionally influenced by one route than the opposite, and discusses the semi-permanent effects of every influence route.[12] While there could also be extra theories of influence, the ELM seems to be unambiguously suited to our exploration of the "black box" of influence inside the Fintech acceptance context that till now has for the most part eluded the acceptance literature, and fills a living gap in Fintech acceptance analysis. The role of influence processes in shaping human perceptions and behavior has been examined by dual-process theories within the psychology literature. The same as IDT, dual-process

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theories recommend that external data is that the primary driver of perspective amendment and sequent behavior amendment. Such information introduces us to new prospects, causes them to canvass their previous beliefs and attitudes, and probably changes existent behaviors. However, unlike IDT, dual-process theories counsel that social judgments aren't continually supported strenuous process of judgment-relevant information, however will typically be supported by the less strenuous process of heuristic cues. These two different processes of angle formation, particularly a lot of versus less strenuous process of data, form the core of all dual-process theories. Further, dual-process theories additionally specify conditions that when each of the two different processes is probably going to be invoked.

Figure: Elaboration-likelihood model ELM is created supported the results data processing via the subsequent two methods in line with the attitude of users: a message recipient through the central path completely examines new information, and assesses its blessings and downsides, and implications, whereas in distinction, an individual receiving the peripheral path chooses to fleetly settle for or deny a service while not active thinking. Receiver’s with the peripheral path conduct broad psychological feature thinking, however they're invariably stricken by the peripheral cue, that modify them to create speedy choices. 19

The central route needs an individual to suppose critically concerning issue-related arguments in an informational message and scrutinize the relative connection of these arguments before forming an enlightened judgment concerning the target behavior. In Fintech acceptance contexts, such arguments could ask the potential edges of system acceptance, comparison of other systems, convenience and quality of system support, and/or prices of and returns from system acceptance. The peripheral route involves less cognitive effort, wherever subjects consider cues concerning the target behavior, like variety of previous users, endorsements from Fintech specialists, and likeability or affinity toward the endorser, instead of on the standard of arguments, in perspective formation. The central and peripheral routes area unit distinct in a minimum of 3 ways. First, the 2 routes method differing types of knowledge. The central route processes message-related arguments, whereas the peripheral route processes cues. Second, the cognitive effort concerned in informatics is way higher within the central route than within the peripheral route. The central route needs thoughtful comprehension of the arguments given, analysis of the standard of these arguments, associate degreed combination of multiple and typically conflicting arguments into an overall appraising judgment, whereas the peripheral route is a smaller amount exacting in this it simply needs subjects' association with salient positive or negative cues associated with the perspective object[11]. Third, perception changes evoked via the central route area unit typically are a lot more stable, show a lot of enduring character, and a lot of long behaviors since they're supported deliberate and thoughtful thought of relevant arguments[7]. In distinction, changes evoked via peripheral cues tend to be less persistent, liable to counterinfluence, and fewer prophetic of future behaviors.

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LASIC PRINCIPLE

ATTRIBUTES

“Low profit margin is a key characteristic of successful FinTech businesses. In a world where there is widespread internet access, where information and services are readily available for free, users not only search for lowest prices, but in many cases, are even unwilling to pay for some services or products, such as video streaming or internet games. High network effects exhibited in such technologies require an initial phase of critical mass accumulation. This is a costly process that demands much marketing effort. Once critical mass is built, monetization becomes possible through channels such as advertising, subscription fees or consumer data analysis. Constant effort is needed to ensure lock-in of users through the reinforcement of network externalities and the increase in switching costs. Profit margins will remain low at the user level. The idea is to obtain a large mass of users and attain profitability through low margins and high volumes. Alternatively, the subsequent buildup of big consumer data can be monetized either through third parties or by creating new products “ Asset light

“Asset light businesses are able to be innovative and scalable without incurring large fixed costs on assets. This results in relatively low marginal costs, which reinforces the first principle of “low profit margin.” One can add on to an existing system (such as the mobile phone) that depreciates quickly but offers an alternative revenue source (such as an internet phone messaging service) at low marginal costs. By riding on existing infrastructure, fixed costs and initial setup costs can be minimized. “

Scalability

“Any FinTech business may start small but needs to be scalable, in order to reap the full benefits of network externalities as described above. One has to be mindful of the fact that when developing technology, it needs to be able to increase in scale without drastically increasing costs or compromising the efficiency of the technology. As more business gets conducted online, the need 21

for physical outlets is greatly reduced. This makes businesses easier to scale. However, developers need to be mindful and ensure that the technology itself is scalable. One such example is the Bitcoin protocol. Although very innovative, the protocol’s implementation is hard to scale, as it is unable to manage a massive amount of transactions at an instantaneous speed. This is also hard to change because of the way the protocol was implemented.”

Innovative

“Successful FinTech businesses also need to be innovative, both in terms of products and operations. With the increasingly widespread use of mobile phones and internet services, much innovation can be made in mobile technologies (such as contactless technologies) in the FinTech space.”

Ease of

“Businesses that are not subject to high compliance regimes will be able to be

compliance

innovative and have lower capital requirement. While financial stability and consumer protection are important for a market to function, tight regulatory environment has its trade-off. In addition to the advantages of a “compliance easy” environment, businesses that receive subsidies or incentives aided by social, financial and economic inclusion agenda brought bout by an anti-income/wealth inequality regime will have an added advantage. The main advantage of operating in a lightly regulated environment is that fewer resources are spent on compliance activities and it encourages innovation.”

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HYPOTHESIS DEVELOPMENT H1: “Personal mobility of payment-type Fintech services has a positive (+) effect on intention to use. ” P. G.Schierz, O. Schilke, and B. W. Wirtz, “Understanding Consumer Acceptance of Mobile Payment Services: An Empirical Analysis”, Electronic Commerce Research and Applications, vol.9, no.3, pp.209-216, 2010. In an analysis conducted by Schierz[4] on the acceptance intention of individuals in European Union who were capable of using mobile devices, quality had a positive impact on acceptance intention. More analysis additionally found that quality affected the acceptance intention relating to mobile services

H2. “Perceived usefulness of payment-type Fintech services has a positive (+) effect on intention to use.” A.Bhattacherjee, and C.Sanford, “Influence Processes for Information Technology Acceptance: An Elaboration Likelihood Model”, MIS quarterly, vol.30, no.4, pp. 805- 825, 2006. Perceived utility is also outlined because the level of utility an exact product or service has for a user. Thus, during this study, the subjective level of utility of payment-type Fintech in existence or task is also outlined as “perceived usefulness”. In studies by Bhattacherjee and Sanford [6] it was found that once a user feels “usefulness” through varied factors, this includes a high impact on “intention to use”. H3: “Perceived ease of use of payment-type Fintech services has a positive (+) effect on intention to use.” J. E.Lee and M. S.Shin, “Factors for the Adoption of Smartphone-based Mobile Banking: On User’s Technology Readiness and Expertise”, Journal of Society for e-Business Studies, vol.16, no.4, pp.155-172, 2011. 23

Perceived easy use is also outlined by the number of effort a user dedicates to an IT platform. However as a result of time may be a constraint condition on users, it refers to once a user feels it's easier to use a particular technology compared to others when time is controlled. In terms of mobile banking, the study by Lee and Shin [8] claimed that technology readiness and specialized information affected easy use, that successively had a sway on intention to use H4: “Credibility of payment-type Fintech services has a positive (+) effect on intention to use.” S. L.Jarvenpaa, J.Tractinsky, and M.Vitale, “Consumer Trust in an Internet Store”, Information Technology and Management, vol.1, no.1/2, pp.45-71, 2000. Jarvenpaa [9] explained credibleness was the major reason variable concerning acceptance. This study proposes the subsequent hypothesis supported a look that adopted credibleness as an element for mobile net services. H5: “Social influence of payment-type Fintech services has a positive (+) effect on intention to use”. Y. S.Foon,and B. C. Y.Fah, “Internet Banking Adoption in Kuala Lumpur:An Application of UTAUT Model”,International Journal of Business and Management, vol. 6, no. 4, pp. 161,2011. One of Fintech services most salient strengths is its large user base. This opens the means for users to simply approach Fintech services and check with the feedback from numerous users, that makes it extremely at risk of social influence. A study by Foon and Fah [11] explicit that in conjunction with promotion conditions and believability, social impact had a big impact on intention to use within the acceptance of net banking.

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H6: “Concern for information privacy of payment-type Fintech services has a negative (-) effect on Intention to use.” C.Van Slyke, J. T.Shim, R.Johnson,and J. J.Jiang, “Concern for Information Privacy and Online Consumer Purchasing”, Journal of the Association for Information Systems, vol.7, no.1, pp.415-444, 2006 Payment-type Fintech service will be outlined as a service supported mobile banking, however the employment of mobile banking raises considerations of outpouring or contraband use of private info. In an exceedingly analysis investigation the link between CFIP and intention to use, Van Slyke [12] found a causative relationship through a medium known as ‘credibility’. H7: “Self-efficacy of payment-type Fintech services has a positive (+) effect on intention to use.” M. Igbaria and J. Iivari, "The Effects of Self-Efficacy on Computer Usage." Omega,vol.23, no.6, pp.587-605, 1995. Igbaria and Iivari [14] conducted an analysis on the result of self-efficacyon use and simplicity use in an study on the connection between self-efficacy and use of a computer on computer users in European country. A study on intention of use of electronic communication services allotted by Shanghai [15] claimed that self-efficacy had a controlling effect on perspective. H8: “Concern for information privacy regarding Fintech services has a moderating effect on intention to use” C.Van Slyke, J. T.Shim, R.Johnson,and J. J.Jiang, “Concern for Information Privacy and Online Consumer Purchasing”, Journal of the Association for Information Systems, vol.7, no.1, pp.415-444, 2006. C. M.Angst and R.Agarwal, “Adoption of Electronic Health records in the Presence of Privacy Concerns: The Elaboration Likelihood Model and Individual Persuasion”, MIS quarterly, vol. 33, no. 2, pp.339-370, 2009.

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In an analysis on the connection between privacy of a closed type SNS and continuous intention to use, Lim & Kang [12] found that privacy issues had a weakening result on perceived psychological privacy, believability and advantages. Perceived expectation and self-efficacy compel positive perspective when deciding an explicit action, and within the finish, have an impression on user satisfaction and intention to use. Angst and Ararwal [16] conducted a study on the consent intention of Electronic Health Records and located that CFIP had a weakening result on subject range, issue participation and perspective. Within the study on the connection between privacy and continued intention to use expressed that privacy issues had a weakening result on perceived psychological privacy, believability and advantages. H9: “Self-efficacy of Fintech services has a moderating effect.” C. A.Murphy.“Assessment of Computer Self-efficacy: Instrument development and validation”, ERIC Document (2nd Ed.), 1988. Murphy [18] claimed that expectations associate with self-efficacy in an individual’s state of mind enabled him to own a positive perspective to decide on a behavior, and as a result, they'd a control on user satisfaction and intention to use. Moreover, once employing a bound system, someone with high self-efficacy can demonstrate high confidence in usage capability, fancy asking queries and check out to interpret data in keeping with one’s own judgment. Against this, someone with low self-efficacy can show low confidence in one’s capability to use the system and can be powerfully inclined to simply accept a definite piece of data conferred as is than question it.

Factors Analysis Among the on top of determinants of Fintech acceptance, social norm and service credibleness is are associated with external influence. Social norm (also referred to as subjective norm or social influence) is outlined because the extent to that members of a social network (e.g., peers, colleagues, members of the family, or different referents) influence one another's behavior to evolve to the community's activity patterns [12]. Davis[4] removed social norms from TAM on grounds that it's by trial and error, nonsignificant and doubtless less relevant within the Fintech acceptance context, however resultant studies have other it back to the model. Significantly,

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social norm suggests that community norms concerning a target behavior ought to exist before new users are often liberal into that behavior, and therefore it cannot justify why new technologies, that community norms might not however exist, are often accepted by a user population. TAM's inclusion of "external variables" as predictors of user perceptions left open the likelihood that external influence from secondary sources, like amendment agents or structure managers, should still impact Fintech acceptance, albeit mediated by user perceptions. Still, TAM/TPB based mostly analysis doesn't justify why any such external influence might occur or explicate the social science method of influence. This communication is plausible to form potential adopters' perceptions of key innovation attributes like its relative advantage, complexity, and compatibility with existing work procedures, thereby motivating their acceptance selections. Subsequent IDT analysis has examined a spread of mass media channels (e.g., news media, experts) and social channels (e.g., colleagues, family members) that function the conduits of data and influence, and studied the impacts of those channels on perceived Fintech attributes. IDT additionally suggests that communication channels might have differential effects across the user population in this that a lot of innovative early adopters are possibly actuated by mass media whereas the less innovative late adopters swear upon the efficacy of a lot of on social channels.

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RESEARCH MODEL Research Design An extensive literature review was conducted to gather information and comprehensive analysis of all existing publicly available information was referred in the report. The approach that was followed for the needs of this analysis was the inductive one. For the needs of this analysis, comprehensive interviews were used. Comprehensive interviews were personal/unstructured interviews with the aim of developing an in-depth understanding of all key challenges and opportunities within the sector. The hypothesized ELM-based influence model of Fintech acceptance was tested by trial and error employing a survey study conducted on-line. The strategy of purposive sampling was employed to develop the sample of the analysis under discussion. The seven constructs of interest to this study were personal mobility, Usefulness, Relative Ease of Use, Service Credibility, Social Influence. All constructs were measured using multiple-item perceptual scales, using pre-validated instruments from prior research wherever possible, and reworded to relate specifically to the context of Fintech acceptance. Individual scale items are listed within the appendix. Relative utility was measured using five item Likert scaled things that tapped into subjects' perceptions of productivity, performance, and effectiveness gains from Fintech acceptance, and overall utility. Service quality was assessed employing a changed version of five-item Likert scale. 3 things from the initial scale that tapped into subjects' perception of the source’s wisdom, expertise, and trustiness were maintained, whereas the dependableness item was modified to quality. Finally, Self-Efficacy and Concern for Privacy info were measured with 2 item Likertscaled items.

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Based on the hypotheses formed in this study, the below research model was developed for evaluating the customer insights:

Concern for Information Privacy

Central Route

H6(-)

Personal Mobility

Relative Usefulnes s

H1(+) H2(+)

Relative Ease of Use

Accessibility

H3(+) H4(+) Intention to Use

Peripheral Route

Service Credibilit y

H4(+)

H5(+) Social Influence

Self Efficacy

H7(+)

The subsequent step for our data analysis was to examine the strength and significance of each of our hypothesized independent variable effects. This analysis was done using two PLS models. The first model examined hypotheses H1 through H7for their main effects, while the second 29

model assessed the moderating effects specified in H8 through H9. Results of the analysis for each phase, including standardized path coefficients, path significances, and variance explained (R2 value) for each dependent variable, are presented below

Table 1: Result of Path Analysis

Path H1 H2 H3 H4 H5

H6 H7

H8

H9

Personal-Mobility-> Intention of Use Service Usefulness -> Intention of Use Ease of Use -> Intention of Use Credibility -> Intention of Use Social Influence -> Intention of Use (Moderator) CFIP -> Intention of Use Self-efficacy -> Intention of Use (Interaction) Personal Mobility* CFIP -> Intention of Use Usefulness*CFIP-> Intention of Use Ease of Use*CFIP > Intention of Use Credibility*CFIP -> Intention of Use Social Influence* CFIP -> Intention of Use Personal Mobility* Self efficacy -> Intention of Use

Results Estimate 0.069

t 1.353

0.231

3.760

0.232

4.553

0.218

3.410

0.102

0.044

-0.090

-1.950

0.011

0.231

-0.086

0.643

-.0124

1.793

0.086

0.517

0.165

0.299

0.088

0.845

0.137

1.696

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Usefulness*Self efficacy -> Intention of Use Ease of Use*Self efficacy -> Intention of Use Credibility*Self efficacy -> Intention of Use Social Influence* Self efficacy -> Intention of Use

0.052

1.347

0.094

2.094

0.102

2.114

0.102

2.087

Results of Path Analysis-SPSS Model

Mobility is one in all the foremost crucial factors in mobile services. However, the very fact that quality failed to have a sway on intention to use implies that it's not essentially appealing to a user once finishing up a transaction.

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The most crucial factors in acceptance during this study were utility and simple use, and that they support the research [7]. What is more, it implies that swift registration, simple use and a convenient UI/UX surroundings might be the most important factors in acceptance for potential users of Fintech services. That being the case, an easy usage procedure and improved convenience, beside enhancements like liberation of economic services, are imperative in promoting this sort of Fintech service. In addition, self-efficacy incorporates a vital impact on intention to use and, as a results of this study, it had been additionally found to own a moderating impact. This means that the IT-savvy generation might like Fintech. In keeping with the results of the Millennials Disruption Index, a three-year study conducted by Scratch, media big Viacom’s artistic practice division, in 2014 (73% of users had high expectations for money services of IT businesses, like Google), and increasing convenience and effectiveness in Fintech services were seemingly to fulfill the expectations of potential shoppers. Lastly, social influence and intention to use had a positive relationship. The characteristics of the social influence variable are connected to it of a platform. This is often as a result of all services are influenced by network externalities. In alternative words, if the put in base will increase, a lot of users would adopt them. Considering this, a policy to decisively connect completely different services and lower entry barriers is critical Through this we have seen that central path had a comparatively higher impact compared to the peripheral path. So as to invigorate payment-type Fintech services, convenience and value ought to be unceasingly improved [9] .This demand the deregulating of various sectors, together with monetary services, communication, e-payment and e-banking.

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INSIGHTS Breakout FinTech Segments

All the segments of Indian FinTech have started gaining ground albeit to totally different extents, thanks to totally different underlying characteristics that impact measurability, adoption and viability. Moreover, not all the segments are seemingly to breakout at identical time. So as to assess the breakout potential, in addition because the temporal arrangement of breakout. The breakout assessment framework for the Indian FinTech market, drawing from the learnings of this paper. For instance in 2017, the digital payments phase has clearly witnessed a breakout thanks to a bunch of business, market and extraneous regulative reasons as well as a push towards digital payments post conclusion. The digital payments phase weighs absolutely on most of the characteristics within the framework. The framework qualitatively grades the twenty FinTech segments across the seven characteristics on 3 parameters (High, Medium and Low) The framework aims to handle the issues across a spread of business aspects together with measurability, business and in operation model alignment, addressing new market opportunities, ability to form and serve new market segments, collaborating and partnering with banks. Using the framework, we've analyzed numerous aspects of companies and consulted trade participants to grasp their breakout potential. The areas marked in darker shades indicate the next chance of breakout when put next to different FinTech segments. Supported the careful analysis lined after, digital payments and alternate disposal emerge because the FinTech segments with the stronger breakout potential. A couple of the segments together with crypto currency and InsurTech rank lower within the Indian market context, however globally these segments most likely have a similar chance of breakout when put next to a couple of segments that area unit rated higher within the Indian context.

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Indian Fintech Breakout-Framework Design

LASIC PRINCIPLESUPPLY SIDE DRIVER

Low Profit Margin

Asset Light

Innovative

ELM BASEDCONSUMER SIDE DRIVER

FINTECH BREAKOUT CHARACTERICS

STRATEGIC THEME

Accessibility

FinTech companies that are addressing areas and functions where customer friction meets largest profit pools (economic value)

Unique Value Proposition

Usefulness

FinTech companies that employ business models that are platform based, modular, data intensive, and capital light to start with

Accessible Business Model Design

Ease of Use

Fostering FinTech companies that actively shape customer Innovative and user behaviors, thus resulting in long-term Customer structural change of the financial services industry Behavior

Compliance Easy

FinTech companies operating with significant legacy issues , prevalence of conventional business models, that lack scalability

Scalable

FinTech providers that offer services to the underserved population using sophisticated capabilities on viable basis

Overall Market Growth

Scale of Colaboration between stakeholders

Social Influence

FinTech companies that actively collaborate with Banks and other FIs and also operate within the Government Regulations regulatory purview of regulators

Service Credentials

FinTech companies that target customers and make curated offers through use of analytics and alternative / big data sources

Leveraging Data and Analytics

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Fintech Adoption-Other Customer Insights

INDIA India Money Transfer and Payments 80% 60% 40% Insurance

Financial Planning

20% 0%

Borrowing

Savings and Investments

Figure: Fintech Adoption among digitally active customers

AGE BRACKETS

INCOME BRACKETS

Age Brackets

Income Brackets

>65

>80

45-64

30-80

25-44

15-30

18-24

<15 0%

20%

40%

60%

80%

0%

20%

40%

60%

80%

100%

The above analysis provides 2 key insights relating to influence processes: (1) External influence plays a vital role within the formation of potential users' Fintech acceptance perceptions and ultimately in shaping their acceptance behaviors (2) Identical influence could engender totally differential effects across different user teams.

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Despite these insights, previous analysis still provides terribly restricted understanding of the character, patterns, and outcomes of influence method for a minimum of 3 reasons. First, it doesn't justify what varieties of data are simplest in influencing user perceptions. IDT distinguishes between mass media and social channels, however doesn't distinguish between the informational content communicated by those communication channels (e.g., what quantity Fintech-related detail is required to win over a possible adopter) or data sources inside every channel (e.g., the person writing the review). Presumably, not all detail content or information sources are equally effective in shaping users' perceptions regarding new technology. As an example, some tend to be influenced a lot of by experts' suggestions, whereas others could believe a lot of on the explanation or proof provided. Hence, info content and supply can also be as necessary in motivating Fintech acceptance because the communication presenting such content. Second, IDT observes that totally different user teams (early versus late adopters) respond otherwise to different channels (mass media or interpersonal), however doesn't make a case for why. The key distinction between early and late adopters is their originality, that is outlined as an outcome variable reflective adopters' temporal arrangement of adoption (i.e., a lot of innovative users square measure early adopters), instead of a causative driver of their adoption behavior. Further, IDT cannot make a case for why some people could also be early adopters of a Fintech service however late adopters of others. Hence, IDT is of restricted facilitate in predicting ex ante however Fintech acceptance patterns might vary across a population of potential users supported the character of external influence. In summary, previous analysis acknowledges that external influence might play a crucial role in shaping users' perceptions associated with Fintech acceptance, however doesn't cut into into the dynamics of the influence method and is so of restricted help in unraveling the complexities of influence patterns and effects. This study addresses the higher than gaps within the TAM/TPB and IDT literatures by elaborating 2 various suggests that of influence, explaining that influence method is only for a given usage context, and presenting an easy however helpful theoretical model that may function the idea for more exploration of the role of influence in Fintech acceptance. 36

IDENTIFYING BREAKOUT SEGMENTS IN FINTECH “ Alternate Lending Evaluation using Proposed Framework Areas

Fintech Segments

Credit

Unique

Accessible

Fostering

Overall

Scale

Value

Business

Innovative

Market

Collaboration

of

Proposition

Model

Customer

Growth

between

Design

Behavior

Government

Leveraging

Regulations

Data

and

Analytics

stakeholders

Peer-to-Peer Lending Crowd Funding Market Place for Loans Credit Scoring Platforms

HIGH

MEDIUM

LOW

Within alternative lenders, peer-to-peer lenders and market place lending platforms are likely to breakout faster, as these lenders target profitable niches of Indian borrower segments, pioneer new business models by having only digital presence, target underserved market segments, and shape user behavior by gaining trust. “ Peer-to-Peer Lending “ Peer-to-peer lending is an innovative model for transferring credit risk from banks and financial institutions, dispersing it among individual lenders. These lenders are typically individuals and households with surplus funds and savings who are seeking better returns. Online P2P platforms significantly address the key areas of customer friction. “ P2P platforms have been able to attract borrowers mainly due to an easy supplication process and quicker turnaround times. Moreover, the convenience offered by these platforms is valued by borrowers and as inferred from borrower responses, interest rates are not the sole criteria for

37

borrowers. “ However as expected, financial returns (from lending) remain the top most reason why individual lenders use P2P platforms, along with seeking diversification in investment avenues. “ Peer-to-Peer Lending-Results from Consumer Survey

REASONS FOR USING THE SERVICES-EASE OF USE

REASONS FOR USING THE SERVICESUSEFULNESS

Reasons for Using the Services-Ease of Use

Reasons for Using the Services-Usefulness

90% 80% 70% 60% 50% 40% 30% 20% 10% 0%

80% 70% 60% 50% 40% 30% 20% 10% 0%

OTHER FACTORS DRIVING ADOPTION Other Factors Driving Adoption Recommendation from Banker/Advisor Distrust of Banks Recommendation from Friend/Colleague Competitive Rates 0%

10%

20%

30%

40%

50%

60%

70%

80%

38

Peer-to-Peer Lending-Results from Stakeholder Interviews “ Two different business models have emerged in the P2P lending segment. Currently players have adopted either the ‘direct disbursal model’ or the ‘partner assisted disbursal model’. 01. Direct disbursal model – The P2P platform directly matches the requirements of borrowers and lenders and is similar to global P2P platforms. Its current focus is on the personal loans segment for urban, educated and middle class customers, who understand the marketplace model and transact online. 02. Partner assisted disbursal model – In this model P2P platforms tie-up with a field partner (local NGO or Micro Financer) to manage customer acquisition, disbursement, and collections for a fee. The P2P platform is primarily responsible for onboarding lenders and offering matching services. “

Figure: P2P Business Models in India Direct Disbursal

Partner Disbursal

Lender Return

14.5-17%

8-10%

Net Platform Operating Cost

4-4.5%

0.5-1%

39

Platform Margin

1.5-2%

1-2%

Partner Commission

-

9-10%

Interest Rate available to

20-24%

19-23%

borrower

Payments “ Evaluation using Proposed Framework Areas

Fintech Segments

Payments

Unique

Accessible

Fostering

Overall

Scale

Value

Business

Innovative

Market

Collaboration

of

Proposition

Model

Customer

Growth

between

Design

Behavior

Government

Leveraging

Regulations

Data

and

Analytics

stakeholders

M-Wallets and PPIs Merchant Payments and PoS Services International Remittance

HIGH

MEDIUM

LOW

M-Wallets and PPIs Digital payments in India are undergoing a revolution. A combination of factors are disrupting the payments landscape, as India, in the black swan event of “demonetization”, transitions to a ‘less cash society’. Payments infrastructure in India has significantly evolved in the past 12-18 months, with new payments modes and interfaces including UPI, BHIM and Bharat QR Code being introduced to drive digital transactions. Average monthly digital transactions have crossed a Billion transactions in 2017. Excluding NEFT transactions, PPI transactions contribute nearly a

40

quarter in digital retail transactions. Average monthly PPI transactions have grown more than five times in the past year. “

Figure: Online Transaction Share

100% growth in digital transactions post demonetization, resulted in India’s cash to GDP ratio coming down to single digits from the pre demonetization figure of 10.6%.

Figure: Monthly M-Wallet Transaction Volume(INR B)

Few payments FinTech companies are leveraging these developments to pivot their business models and change their focus from consumer payments to enabling banks, merchants, and other payment intermediaries. “ Moreover, all of these newly introduced instruments, channels, and interfaces do offer a better and effective payments architecture, but still are mere enablers to the payments business. None of these in themselves are likely to create new business propositions – something payments FinTech companies aspire and aim for. “

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Merchant Payments and PoS Services The payments sector in India has relatively low barriers to entry compared to other financial services, and perhaps, that could be one of the reasons for the fast pace of innovations in this “segment. Going forward, partnerships with large merchants and an unerring focus to drive the unit transaction cost to near zero, will be the two decisive factors for payment FinTech companies. Cross border payments In the past 2-3 years, few Indian FinTech players have setup bitcoin exchanges in India to facilitate the purchase and use of bitcoin as an alternate currency for paying for mobile credit, data card and DTH bills. Global block chain startups including Ripple have partnered with Indian FinTech Companies to offer their proprietary blockchain enabled currency – XRP. Ripple uses the same currency (XRP) to undertake international remittance business by setting up exchanges in host markets.” Unlike other popular cryptocurrencies, XRP is a pre-mined currency used for settlement and it has the advantage of increased settlement speed over other cryptocurrencies. “The use case of international remittance for blockchain technology is one of the promising use cases for the Indian market. India is the biggest market for remittances, with over $62 Bn sent to India from abroad in 2016. The likely benefits of using blockchain in enabling cross border payments are described in the illustration below: “

Figure: Blockchain-Benefits under Cross-Border Payments

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Investment Management “ Evaluation using Proposed Framework Areas

Fintech Segments

Investment

Unique

Accessible

Fostering

Overall

Scale

Value

Business

Innovative

Market

Collaboration

of

Proposition

Model

Customer

Growth

between

Design

Behavior

Government

Leveraging

Regulations

Data

and

Analytics

stakeholders

Robo Advisors

Management Discount Brokers Online Financial Advisors

HIGH

MEDIUM

LOW

Online Financial Advisors With innovation, advisory services are likely to break off from the product. As customers move to automated platforms, fewer investment management products will be sold through own advisory channels. This is likely to result in increased competition amongst existing players in specialized segments or services. Data is seen as a major disruptor in investment management, empowering investors and service providers alike. “ Roboadvisory The current penetration of investment management services is very low, as most investors prefer to channel their savings in deposits, through banks. Driven by technology evolution, a few FinTech companies have introduced services, offering automated financial advisory services “ based on a pre-defined set of rules and algorithms at a significantly reduced cost. These platforms leverage customer information and run algorithms to develop automated portfolio allocation. The share of assets managed by robo-advisers in India is still low (less than 1% of assets), and their services are mostly targeted toward younger and financial savvy investors. “ 43

Discount Brokers These FinTech companies offer complete online and digital trade execution facilities, at a fraction of the fees, as compared to traditional brokers. Discount brokers have no overheads of ” physical branches, large research and onboarding teams, and pass on the benefits to the investors who can trade paying a very small fees. Specialized discount brokers have developed customized APIs that are extended to sub-brokers and retail investors for setting up customized trading platforms. A few of the discount brokers have also partnered with specialized equity screeners, to offer investor stock screening services, based on thematic and strategic research. “

Banktech “ Evaluation using Proposed Framework Areas

Fintech Segments

Unique

Accessible

Fostering

Overall

Scale

Value

Business

Innovative

Market

Collaboration

of

Proposition

Model

Customer

Growth

between

Design

Behavior

Government

Leveraging

Regulations

Data

and

Analytics

stakeholders

Big Data, AI and Robotics BankTech Blockchain Customer Onboarding Platforms

HIGH

MEDIUM

LOW

Customer Onboarding A few of the FinTech companies focusing on customer onboarding and authentication solutions in India have received recognition, not only from partner banks and NBFCs, but also from regulatory authorities. FinTech companies are also deploying Artificial Intelligence (AI) and blockchain to authenticate, validate identity and undertake background checks on customers.

44

These capabilities are likely to improve the overall quality of digital onboarding for both, incumbent banks, as well as, other FinTech companies. “ Blockchain ” Within the bank-tech segment, globally, blockchain remains one of the breakout candidates in the short term; however, in India, application of blockchain is currently limited to a few proof of concepts conducted by the regulator and a few private banks. Apart from trade finance, blockchain technology can be used for facilitating cross border payments, insurance claim processing, equity trade settlements, syndicated loans with multiple lenders, and asset hypothecation. In addition to the benefits, most of these use cases will result in cost optimization across the financial services industry. “

Figure: Use Cases of Blockchain Technology in Banking

Big Data, AI and Robotics Emerging FinTech segments in the areas of Artificial Intelligence (AI), Machine Learning (ML) and robotics are emerging, albeit in the nascent stages. Most of these FinTech companies are working with banking partners to improve current operations and servicing. A few private sector “ banks have been working with FinTech companies to automate certain customer servicing activities in call centers. Globally, financial services are also adopting AI for compliance, anti-

45

money laundering and risk management. However, some of these underlying technologies remain a niche in India.”

InsurTech Evaluation using Proposed Framework Areas

Fintech Segments

Unique

Accessible

Fostering

Overall

Scale

of

Value

Business

Innovative

Market

Collaboration

Proposition

Model

Customer

Growth

between

Design

Behavior

Government

Leveraging

Regulations

Data

and

Analytics

stakeholders

Insurance Aggregators Insure IOT and Tech

Wearables

HIGH

MEDIUM

LOW

Despite strong improvement in penetration and density in the last 10 years, India largely remains an under-penetrated market.” The market today is primarily dependent on push, tax incentives, and mandatory buying for sales. InsurTech primarily aims at enabling a better reach of insurance products & services, as well as a greater personalization of insurance products, and proactive management of key risks. Insurance aggregators Insurance aggregators are essentially a digital distribution channel allowing customers to compare scope of coverage, term, premium, and terms relevant for customers to enable them to

46

make an informed decision. With a penetration of over 400 Mn Smart Phones by 2020, the digital insurance channel will be an important medium for distribution of Insurance products. “ IOT and Wearables “Increasing adoption of connected devices e.g. telematics, and wearables presents an opportunity for Insurers to better understand customers and personalized customer engagement. This will require Insurers to work closely with device and service providers. It will require close partnerships between insurers, re-insurers and product/ platform companies.” For example, Max Bupa recently announced an alliance with GOQII, a wearable and a fitness technology player, and Swiss RE, a global re-insurer.

Figure: Evolving Nature of Insure Tech

47

Personal Finance Management “ Evaluation using Proposed Framework Areas

Fintech Segments

Personal

Tax Filling and

Finance

Processing

Unique

Accessible

Fostering

Overall

Scale

Value

Business

Innovative

Market

Collaboration

of

Proposition

Model

Customer

Growth

between

Design

Behavior

Government

Leveraging

Regulations

Data

and

Analytics

stakeholders

Management Spend Management and Financial Planning Credit Services

HIGH

MEDIUM

LOW

Personal Finance Management refers to a a platform that helps the user manage his/ her money. Managing, spending, and investing money are important decisions that have a profound impact on the financial health of the individual. Most customers know the basics of money management, but are not financially savvy enough to manage on their own. This is where the personal finance management app comes into the picture. These apps have gained popularity in the last couple of years and they assist customers in keeping a watch on their expenses at a single place. Key enablers in support of the personal finance management app are: “ 01. Regulations: The RBI, in its guidelines has instructed banks to send notifications on every transaction to customers. Personal Finance Management apps have leveraged this, to provide an overview of all spends of a customer.

02. Data scraping: Another important factor in the development of the personal finance management app is the technology of data scraping. Data scraping has enabled personal finance management apps to read messages of customers, and analyze transactions. 48

CONCLUSION Indian FinTech companies could address a few of the critical structural issues afflicting Indian financial services - increase outreach, improve customer experience, reduce operational friction and foster adoption and usage of the digital channel. Legacy prone processes and higher operating cost models of incumbent banks and financial service providers will give digital FinTech companies an edge, as banks play catch-up with these more nimble and innovative startups. The opportunity for FinTech lies in expanding the market, shaping customer behavior, and effecting long term changes in the financial industry. Indian FinTech companies have the potential to reshape the financial services landscape in three ways: •

The FinTech startups are likely to reduce costs and improve quality of financial services. Not being burdened with legacy operations, IT systems, and expensive physical networks, benefits of leaner operating models can be passed on to customers.

•

The FinTech industry will develop unique and innovative models of assessing risks. Leveraging big data, machine learning, and alternative data to underwrite credit and develop credit scores for customers with limited credit history, will improve the penetration of financial services in India.

•

FinTech will create a more diverse, secured and stable financial services landscape. FinTech companies are less homogenous than incumbent banks, and offer great learning templates to improve, both, capabilities and culture.

Just as incumbents have a lot to learn from emerging FinTech companies. Fintech companies can also learn and adopt best practices around risk and internal controls, operational excellence, compliance culture, and employee engagement, that has stood the test of time for most the banks, and financial services providers in India.

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