By Mohan Ramaswamy as Co-Founder and CEO of Rubix Data Sciences
The competitive landscape of the Indian banking sector will have a new player from this year. This will be the former PMC bank now acquired by a JV between BharatPe and Centrum Group. While mergers and acquisitions in the financial sector are commonplace, what stands out from the above case is that for the first time in India, a FinTech company (BharatPe) has acquired a bank with the blessing of the RBI . This is a clear indication of how FinTechs are no longer seen as a mere extension of banking services or simply providers of digital infrastructure. This change in perception is driven by the explosion of the FinTech sector in India. According to venture capital firm BlinC Investment Management, India has the third largest FinTech ecosystem in the world with a very high adoption rate.
It is therefore not surprising that the Indian banking system is undergoing a rapid digital transformation. One area of this transformation is the extensive use of data science technologies. The year 2022 is expected to be a pivotal year in the adoption and integration of data science in the traditional banking sector. Here is an overview of the main areas where data science will help accelerate Indian banking:
Risk is an intrinsic component of the loan. Therefore, it is essential to identify and quantify risk factors before making loan, credit or investment decisions. Data science-based risk analysis helps organize and analyze unstructured data, which forms the bulk of a company’s risk information, and greatly reduces the likelihood of human error.
For example, if a bank needed to perform risk analysis of a potential commercial borrower before lending, smart data-driven tools can quickly analyze large amounts of internal and external borrower data to provide insight into company and its risk profile. , as well as the background of its directors or owners. Data-driven risk models can highlight a company’s financial weaknesses, provide a credit score, and recommend credit limits. Based on the credit score generated by the risk assessment model, the lender can decide whether the business is creditworthy.
Even in the case of loans that have already been disbursed, data science-based credit risk monitoring tools can monitor and provide early warning signals (EWS) of any deterioration in business health. These data-driven EWS tools provide dynamic credit scores that automatically change based on new data points that the tools have collected. Banks can take quick action to reduce their exposure to a company in the event that emerging data about it is negative and its credit rating has dropped.
With the spread of the COVID-19 pandemic, banks are having to do many of their processes online, and that includes due diligence before lending. In such cases, banks can deploy data science to create robust risk algorithms that use data from a myriad of structured and unstructured sources, including social media. Data points collected for consumer loans include location, age, gender, income, job type, etc.
For B2B loans, the algorithm uses firmographic, identity, financial, compliance, legal and financial data. The availability of a wide range of data points helps banks better understand the behavior of their borrowers, thereby ensuring lower credit risk. Loan quality improves dramatically when data-driven models are deployed, as loans are approved based on objective data verifications. Data-driven lending solutions help banks identify and engage with the right customer profiles throughout the borrower lifecycle, improving their profitability. Digital banking, where all processes are done online, also benefits immensely from data science tools.
The other side of the coin of digital transformation in the banking sector is the increase in the number of frauds. According to RBI data, India recorded more than 229 bank frauds per day in fiscal year 2021. There is also a massive amount of frauds involving UPI transactions, most of which go unreported. However, the good news is that data science-based fraud detection tools can analyze vast swathes of know-your-customer (KYC) and payment transaction data to identify patterns in fraudulent transactions and flag activity. suspicious. This helps banks in fraud prevention as well as anti-money laundering (AML) activities.
Here is an example of a bank’s digital fraud detection tools in action: if an unusually high value transaction occurs that does not match the account’s transaction history and the time and place of the transaction are unusual, it is marked in red. This transaction can now only proceed when the account holder confirms the details, usually with an OTP. In the case of new accounts, these digital tools can drill down to see if multiple accounts have been opened in a short period of time using similar data; very often this is done in order to facilitate fraud or money laundering. Reporting these cases leads to more detailed KYC and AML checks of account holders.
Predicting Customer Lifetime Value (CLV)
CLV is a metric that predicts how much and for how long a customer will be valuable to a business. Banks are increasingly using this measure to make projections about the growth and profitability of their business. CLV also helps banks decide which customer relationships to invest in. Data science tools help banks get a 360-degree view of every customer because they can analyze the vast and varied data the bank collects about every customer. This helps in more accurate CLV prediction.
Financial inclusion via FinTech
Financial inclusion is one of the major challenges facing India and its banking sector. It also presents a tremendous opportunity for growth by improving access to finance for traditionally marginalized segments of the population. In the past, lack of access to this segment of the population, insufficient data to design products tailored to this demographic, and the resulting lack of trust has driven most banks away, with the exception of PSU banks.
However, data and technology have changed all of that by providing banks with the ability to leverage unstructured and alternative data to better understand the behavior of various socioeconomic segments and demographic groups.
India is one of the few major economies in the world to have built Digital Public Goods (DPG). Popularly known as the “India Stack”, the series of volunteer-run software platforms are central to the Indian government’s digitization agenda and financial inclusion goals. The JAM trinity – Jan Dhan, Aadhaar, Mobile – has, in a short time, brought a large swath of the population into the mainstream of finance and put them on the Digital India bandwagon. The Direct Benefits Transfer (DBT) enabled by JAM is changing the face of finance in rural India.
India’s new DPG architecture has laid the foundation for more inclusive financial integration with services in regional languages, tailored insurance products and personalized services at the individual level. After the massive success of the Unified Payment Interface (UPI) which allows money to be transferred in less than 6 seconds, many more exciting innovations in banking and finance are on the horizon.
With the Indian government’s massive push for financial inclusion as well as the digitization of payments, data analytics has a huge role to play in increasing revenue, improving customer experience, streamlining costs and predicting risk. for banks. There has been a huge explosion of data in the Indian financial services sector, and the adoption of data analytics is important in making banking services more convenient and egalitarian while tailoring financial products to user needs. This will help achieve financial inclusion and digitalization goals while accelerating growth and improving the profitability of Indian banks.
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