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GIGO in FinTech

The term FinTech is all over the business media. One does not really know when exactly it came into such ubiquitous usage. Separating the real power of FinTech and the hype around it is a million dollar task.

As I understand, FinTech simply means using technology to deliver or enable financial transactions better. And frankly, innovation in this space has been going on in the traditional banking and other financial sectors for many years, if not decades.

The rise of Retail Financial Services industry meant that millions of accounts, loans, credit cards, transactions, payments were happening through Banks, NBFCs, AMCs, Insurance companies. Given the enormous numbers involved, it became necessary that Technology be used to deliver the product and service the customer. This was the low-hanging fruit and companies duly executed this in the last two decades through Automation – Point of sale machines, ATMs, IVRs, Cards, Mobile/Internet banking etc. This brought in operational efficiencies and therefore brought the costs down.

However, the ‘gold’ to be found in this space was not Operational but Strategic. Not just Automation but Intelligence. Technology can give incredible insights on how customers behave, what they buy, when they buy, how they repay, how they save and spend and a lot more. To extract this ‘gold’, FinTech ventures arose – most of them followed a multidisciplinary approach marrying Marketing, Technology, Data Analytics and Product/Value proposition design.

The crucial element is the data and information gathered about the customer and her financial life. This can come

a) Through data at the point of sale (POS) – say, from application form (demographic, psychographic),   previous transaction history, and

b) From the usage and behavior patterns after the customer uses the product (e.g. current/saving account, loan or Insurance plan).

FinTech companies try to use this data to make better offers, better targeting, better credit underwriting and so on. FinTech can exist as a solo venture or can be incubated within an existing, large business.

Across the world, FinTech trends are at a nascent stage. The biggest hurdle for FinTech to become ‘Intelligent’ is the Integrity of the data going into the models that are built to form Hypothesis, Rules, Algorithms and eventually Strategies.

GIGO is a computer term for “Garbage In, Garbage Out”. The GIGO factor is at play at the moment and therefore, in my opinion, the FinTech’s promise on paper may not get delivered in practice. Of course, this is a journey for many businesses and the space will evolve constantly. Let us examine why GIGO factor is a big hurdle to cross.

The first step in building models and algorithms is to rely on past data about existing customers of a certain product or company, if it is available. Another alternative is to use primary data available in the Industry or directly from customers. In India, past data in most cases is either not available or if available it is incomplete or of questionable quality.

  1. There are many factors that pollute the data and information collected. Let us examine some of them. Proliferation of Agents: In India, very often, customers do not directly deal with the Mutual Fund or the Bank or the Lender or with Insurance. The Agents of these companies fill the forms, do the paperwork for the customer and therefore influence the data that is getting collected. In my experience, for example, Agents influence and direct what data should be filled in “self declaration” boxes, or which contact information should be given (it is not uncommon to find 30-40% false telephone numbers or emails). Even when companies provide direct web portals and mobile Apps, there are instances where people give their user-id and passwords to their agents to operate!
  2. Lack of Professionalism and discipline of sales and operations staff: There is a culture of shoddy work at branches, or sales offices of most companies. The staff skills and habits are poor with respect to collecting data and recording them accurately on system (CRM). This is in contrast with other countries in rest of Asia where the skills and discipline levels are much better.
  3. Not enough modeling and testing in a complex market: FinTech will come into play only when robust and proven models impact the company’s revenue, product and service strategy. For example, one may conclude from behavioral pattern of consumers that they prefer a particular product feature (say a payment term or an MF type), but within that there may be layers of variance hidden e.g. people in Kerala may show polar opposite behavior from people in Maharashtra. Averages in a market like India can be misleading. Broad level models will not help. A full distribution range/clusters need to be peeled open and robust tests need to be done to establish strategies.
  4. Lack of process, tools and systems: Traditionally, financial services Industry is a laggard in investing in adequate systems and tools. Hence, the gathering of data is cumbersome, absent or partial. This hampers the FinTech venture to harness the full power of its proposition. Even FinTech that relies on mobile or web platforms are underinvested in terms of Analytics. One reason is that many times FinTech ventures are founded/designed by techies rather than people with serious real-world experience.

In conclusion, the GIGO factor is a crucial factor to overcome if many of the current FinTech ventures were to become successful.

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