For several years, the financial sector across the world has been undergoing a digital transformation. In a socially distanced world, the pandemic created more opportunities to innovate new digital financial services, while also uncovering the gaps in the Indian financial system, such as the inaccessibility of credit to Micro, Small, and Medium Enterprises (MSMEs). MSMEs are vital cogs in the growth engines of the Indian economy, contributing to about 30% of the GDP.
by Lalit Mehta, Co-founder & CEO, Decimal Technologies
However, during the pandemic, numerous MSMEs have suffered due to the absence of access to credit. The MSME credit gap approximately amounts to a monumental $240 billion due to traditional institutions’ lack of flexibility and their inability to effectively leverage the available user data and viably reach the semi-urban and rural areas.
This, however, is not just a problem, but also an opportunity. There is immense potential in the MSME segment for banking institutions as they keep adopting digital and tech innovations. As we step into the year 2022, let’s look at how this opportunity might play out within the financial sector.
Open banking is a solution that is emerging as a way to completely transform the way credit is disbursed, making it more suited to the financial situations of MSMEs. Open banking refers to a system where banks and other financial institutions allow third parties, such as fintech companies, to access user data via secure Application Program Interfaces (APIs). Not only can the APIs enable fintech partners to build new services that are more efficient and accessible, but also allow traditional financial institutions to offer experiences fit for the digital age.
Open APIs have the capability to help financial institutions digitise the process of lending and address the growing credit demand. They can also help with some much-needed customisation in the offerings, thereby catering to specific needs that might not be addressed in their entirety by a single legacy product. Artificial Intelligence (AI)-based solutions offer flexibility that suits the borrowers’ needs that might not always be feasible for traditional processes to identify or address.
The value of approved and disbursed loans is mainly determined by how an individual or business is likely to pay it back. This is why risk assessment, or determining how likely an individual is to default is critical for the entire sector, and this is where AI and Machine Learning (ML) can change the game. The AI/ ML components employed by FinTech firms can match the customer with the lender without minimal to zero manual intervention, solving one of the key problems of the lending industry- that of risk assessment. This is done with the assistance of detailed, user-friendly credit assessment memos which allow lenders to practice controlled yet faster risk assessment.
With the help of AI and ML, banks can understand how an individual’s or a company’s recent financial behaviour deviates from past behaviour, and therefore get early insights into potential causes of concern. In this scenario, having early insights enables financial providers to take action with a relevant response – i.e., reassessing the approved loan amount or declining a loan.
For years, banks and other lenders have been using computer systems to automate more and more of the loan process. With the massive growth witnessed by businesses on the back of new-age technology, many institutions are now trying to fully automate the process. Adoption of AI results in an enhanced borrower experience and assists in making informed decisions with utmost certainty. It eliminates administrative expenses and delays to maximize the amount of profit for every loan created. Removing human bias, decisions will increasingly be based on verified customer data like their monetary status and accuracy, giving little room for error and helping businesses focus on other aspects of the lending process that still require human attention.
The banks will also have the liberty to consider a more proactive approach towards the onboarding of new customers. During the loan application phase, AI and ML are often used to anticipate credit needs by analyzing credit line usage and understanding historical data patterns. For instance, an agricultural business is likely to have seasonal credit needs; these needs can be modelled to understand typical versus atypical patterns.
Better Banking Experience
An increase in the integration of AI and ML will also mean the elimination of human intervention. Decisions made by humans are almost always influenced by biases which may end in either a poor experience for the customers or losses in terms of loan frauds for financial institutions. AI-driven tools run the available data against a group of rules to work out the borrower’s acceptability, thereby speeding up the process, and also ensuring security for the institution.
By understanding how a company’s recent financial behaviour deviates from past behaviour, banks can detect or create opportunities for expanding their business relationship with the customer – or get early insights into potential causes of concern. In both these scenarios, having early insights enables financial providers to take action with a relevant response – i.e., extending credit proactively or declining a loan.
2022 is set to witness a further increase in the adoption of AI and ML. This will lead to a bridging of the credit gap that the MSMEs are suffering from, resulting in further bolstering of the economy. This will also exponentially enhance customer experience while cutting down on the risks. This New Year will be a better year for banking.