The Impact Of AI On The BFSI Sector

The pace at which technology has been galloping in the last few years is remarkable at the very least. Technological growth has even defied the observations of Moore’s Law in recent times, with powerful simulation processes like Artificial Intelligence (AI) reducing the complexity of data-heavy tasks to irrelevance. The BFSI sector as such is certainly no stranger to technological advancement. Over the years, and quite rightly so, it is this sector that has perhaps benefited the most through technological breakthroughs. This comes as no surprise really, considering the sheer number of customers, the amount of data and the expanse of information that the BFSI sector juggles.

Many Fintech companies these days are actively employing AI and Machine Learning to achieve truly phenomenal results in the lending space. It wasn’t long before consumers had to submit a long list of physical documents to process their loan applications. Before the advent of paperless banking, lending processes involved cumbersome paperwork besides a long list of other pain areas including delayed disbursals, multiple verification calls, personal visits and undue process delays. Technology has stepped-in to eliminate most of these hurdles, and getting a personal loan these days has never been easier. Many Fintech lenders who’re actively leveraging AI have evolved a seamless process where consumers can finish their loan applications and receive funds in as little as 3 minutes! Loan processes these days have been rendered so easy and convenient that ordering a pizza or hailing a cab can appear to be fairly difficult tasks in comparison.

The overall impact that AI has had on the BFSI sector has been quite amazing indeed. Let’s look at some areas where the influence of AI has been particularly pronounced.

Here is how the BFSI sector, particularly the Fintech industry, is being influenced by AI:

Credit risk assessment

Risk assessment is a crucial part in lending, particularly in the Fintech space. Most lending companies that have partnered with banks assess consumer profiles on their own, meaning that they themselves conduct eligibility checks on applicants. In order to qualify for any type of credit, a consumer is expected to be creditworthy by way of having a decent credit score and a clean repayment history. Multiple parameters are considered simultaneously while performing a credit check, and AI assists this process quite impressively by providing accurate assessments of borrower profiles, making it possible for lenders to make accurate credit decisions. AI also does its bit in eliminating human errors to make unbiased decisions on a consumer risk profiles.

Improving operational efficiency

Lending companies that are using AI to optimise their models are also improving their operational efficiency by eliminating roadblocks in several areas including profile verifications, data-driven decision making, consolidating expenditures, and tracking customer transactions.

Fraud detection

A key area where AI has been of significant assistance is in detecting fraud and containing risks emanating from fraud. Banks especially, are today relying heavily on AI to prevent forgery, shady transactions between shell companies, document fraud and cyber crimes. AI particularly comes of use when data from multiple sources needs to be collated, studied, and identified for inconsistencies that could possibly showcase fraudulent activity.

Automation through algorithms and messenger bots

Algorithms that today have the capability to evaluate a consumer’s credit profile in seconds have been made largely possible through the intervention of AI. Let’s say a consumer wants to apply for credit and takes the online application route. Once he provides his details, an algorithm quickly scans his profile by instantly accounting for multiple parameters, subsequently making a lending decision within seconds. Some Fintech lenders have built super-advanced risk-assessment algorithms that do not make lending decisions based on an individual’s credit score alone. Several parameters are brought into the equation and risk is evaluated in a highly precise manner.

Messenger bots

Another AI-powered concept that has attracted attention lately is the use messenger bots to effect seamless communication between lenders and customers. Through messenger bots, consumers can access a ton of information ranging from tracking repayments to checking application statuses and monitoring their accounts. Presently, many Fintech companies are using interactive Chatbots to allow customers to complete their entire application process online, including document submissions and e-signatures.

AI and analytics

AI along with Analytics has been in active use to gain large-scale insights from multiple data fields and make credit decisions. The results obtained from detailed analytics are fed into algorithms and alternative scoring models are gaining deserved prominence. Ai is particularly useful in geographical analytics where lenders seek to identify optimal consumer profiles within the confines of a given geographical area. Lastly, it is through AI and detailed analytics that Fintechs are today evolving advanced algorithms that account for numerous parameters to make the most informed lending decisions.

Portfolio optimisation

To identify the right mix of customers and sport sufficient enough diversity is essential to evolve an efficient and sustainable model. Fintech marketplaces are using AI to match customers with the right lenders, offering them the best loan terms based on their profile indicators.

The author is the Co-founder and CEO of Qbera