In Wishfin’s business model, customers make wishes, and Wishfin shows them the best financial strategy to fund those wishes, whether through savings, investments, or borrowing. It’s difficult for consumers to envision the long time frames of goals like retirement plans. Wishfin came up with ways to analyze consumers, segment them using tech, and understand them by doing things like giving them their credit reports for free to help them understand their credit histories. Such steps let Wishfin enhance their data sets so they can provide consumers better guidance. About 7 million consumers have taken credit reports from Wishfin, and the company helps disburse about 100 million loans annually.
The Rise of Fintech
Fintech dates back to the invention of the ATM: a machine that could interact with consumers and provide them a direct service. In the 1970s and 1980s, banks had to incur servicing costs for consumers visiting bank branches, and the banks had to recoup these costs through fees. With the advent of personal computers in the 1980s, consumers could accomplish many of the same transactions on their computers that they previously had to visit bank branches to do; in response, banks could pass operating savings back to consumers.
With the adoption of mobile phones in the 1990s and 2000s and the parallel rise of the Internet, these capabilities became more sophisticated and more widely distributed. Customers could now see comparisons between financial services and offers and thereby make better decisions with less effort. Over the next decade, bank branches will become obsolete for their current purposes and will take on different customer service functions.
In 2020, fintech filled an immediate need to reduce face-to-face interactions in banking and finance, mandated by the COVID-19 pandemic. For example, onboarding a customer used to require paperwork, identity verification, and other contact-intensive procedures. The pandemic forced institutions to develop non-contact processes and procedures for onboarding, and artificial intelligence (AI) helped solve identity verification and make physical signatures unnecessary for fraud prevention. These changes also had the effect of making these procedures easier for consumers.
Privacy and data security are important issues for consumers. Rishi advises that fintechs limit their requests for personal data to the onboarding process rather than make repeated requests to intrude on customers’ personal data.
The Future of Fintech
In the next few years, fintech will partner deeply with traditional banks so each can benefit from the other’s strengths. Physical locations will be far fewer and less important as the customer experience moves to the consumer’s screen. Institutions will offer more transparency to consumers to let them see where their money is kept, how it has been invested, and what fees the institutions are charging for each service. An example is direct plans for mutual fund investment in India, where consumers have the opportunity to save on expenses by directing their own investments. This transparency opens up access to financial tools and structures to consumers and replaces traditional intermediaries and experts with data-driven services.
Over the next several years, banks and fintech will take up blockchain for transaction security and automation. Today, the direction for this technology in fintech is still unclear, but it is getting clearer every day.
Traditional banks have been driven by consumer marketing, seeking to attract consumers for a well-defined set of products and services. By contrast, fintechs have focused on the technology first, developing capabilities through software, AI, machine learning, and other advances, and then finding the consumers who need those capabilities.
Next, we will be discussing AI trends in Fintech. So let’s get started
AI Trends in Fintech
In this section on AI trends in Fintech, one of those capabilities Rishi mentioned was the example of fraud prevention through identity verification: deep learning now allows digital systems to recognize individuals’ faces even if they have made changes to their appearance (like growing a beard). On Facebook, this technology tags you in any photograph that has your face in it that gets posted. In fintech, it ensures that you and only you have access to your data and your money. AI and deep learning also detect patterns of behavior that indicate attempted fraudulent activity so that the activities can be flagged.
AI and deep learning also allow fintech firms to segment consumers by what they need. For example, consumers just taking up their first financial product will not have a credit history, credit score, or other traditional indicators of being a good credit risk with a high probability of repayment. This consumer segment is underserved – they don’t attract the attention of traditional banks. However, by using AI and deep learning, fintechs can model the behavior of this customer segment and determine what non-traditional indicators show that a consumer is a reasonable risk and likely to be a profitable customer. This modeling is a win-win: the consumer gets access to credit at lower rates, and the fintechs tap into this underserved consumer segment.
One big insight fintechs have developed in recent years is that profitability is not a matter of seeking out customers with perfect credit histories and scores: these constitute a tiny fraction of the market, and they will demand very low interest rates on loans. Profitability for fintechs comes from understanding the true creditworthiness of the rest of the market and tailoring offers that attract many good customers at rates that they can reliably repay with predictable and manageable default rates.
This approach depends on getting lots of reliable data about consumer behaviors so that machine learning can build predictive models for AI decision engines. Some data comes from credit history reports, some comes from data about you on your phone, such as your transaction history and your contacts, and some comes from other sources. Fintechs that can get consumers’ permission to access this data can build the models to segment consumers by ability to repay and likelihood of profitability.
One of the advanced fields of AI development in fintech is the elimination of biases. A rules-based engine might have the rule that a particular minimum credit score is necessary to approve a loan, but deep learning can eliminate that bias and replace it with an understanding of multiple other factors that balance out or even provide better predictions than credit scores.
That was all about AI trends in Fintech.
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Future-Proofing Your Career
While analyzing the AI trends in Fintech, Rishi identified four areas of technology development where fintech needs technology talent:
- Customer onboarding
- UI/UX
- Software structure, such as APIs and architecture
- AI backend
Rishi recommends that technology professionals make lifelong learning a priority because the pace of technology change means that your current skill set will be largely obsolete in just a few years. But on the question of whether AI will make technology professionals themselves obsolete, he points out that every advance in technology has made some forms of work obsolete while creating other new and more productive forms of work. Staying ahead of the AI wave offers technology professionals the opportunity to build new and more interesting careers over time.
Simplilearn supports upskilling in artificial intelligence. Our AI and ML Courses in partnership with Purdue University is an excellent way to develop deep expertise in this field, whether you seek a career in fintech or any of the many industries leveraging AI and deep learning.
