Underwriting Credit in 2023: How location intelligence benefits modern BFSI

TL;DR: The information banks and NBFCs have access to is restricted, and does not substantially mitigate risk. It is because the lending industry relies on credit score analysis as a standard tool of risk assessment. Inherent nuances within those systems create ambiguities that, in turn, leave margin for errors, omissions, and confusion — leaving a large number of people ineligible to obtain loans, even if they are potentially affluent, high earning applicants. Standard systems can be compromised due to its limited ability to provide only a basic understanding of its customers.

A bank or financial institution’s standard lending method can take several weeks (or even months) to analyze, examine, and approve a commercial loan request from a typical consumer. Typically, consumers must visit their branch numerous times and provide an unending amount of paperwork. However, in today’s environment, when anyone can open an account, invest, and make purchases online (with only a few clicks), the commercial lending schedule begins to appear arbitrary and unorganized. For the consumers requesting the loan, waiting periods could be damaging if the funds are required for a negotiation or purchase. The outdated commercial loan processes are also not secure for the lending bank since they tend to ignore the non-traditional elements that may increase risk.

Traditional profile indicators and application processes are gradually being replaced, save public sector banks

Many firms are attempting to unearth new insights by merging business analytics and location data in order to achieve a competitive advantage in the marketplace and mitigate these risks. It is because hyperlocal and spatial analytics technology provides certain context that tables and charts cannot alone deliver. Though there are no ways of 100% predicting which customers are likely to default, location intelligence has helped lending and insurance businesses develop models that help cut down risks significantly. That is why many firms are increasingly attempting to include location analytics into their operations, after being mostly absent from business analytics solutions up until recent years.

Traditional profile indicators and application processes are gradually being replaced, save public sector banks

In today’s technology-driven, customer-centric world, financial institutions can gain a competitive advantage by offering speedy resolutions to customers requesting credit services — essentially getting to the ‘yes’ or ‘no’ — while minimizing and managing risk. Location intelligence proportionately aids in the faster determination of responses to inquiries like,

“Does this property exist?”,

“Is it identified correctly?”,

“What is the income of the person buying houses in affluent localities?”

“Is the property vulnerable to any environmental or demographic conditions that would necessitate a thorough risk assessment, even a higher risk assumption?”

Location intelligence allows you to identify a specific location, ensure its accuracy, and access current, historical, and predictive environmental and demographic data for that location.

Examining the credit risk a consumer comes with requires knowledge of external data and location intelligence, and allows developing models upto a certain probabilistic standard, unlike typical underwriting models that focus on only a few credit criteria. Such models can augment traditional scorecards in consumer profiling in the long run, if made to undergo continuous improvements via our machine learning and artificial intelligence conditioned with big-data stacks.

By building models on more than simple default rates and focusing on outcomes such as profitability and customer lifetime value, insurance and lending institutions can leverage location intelligence and increase their business performance. With that said, location intelligence will become more crucial as risk mitigation and risk concentration analysis become must-haves for BFSI. Location intelligence may very well be a differentiating factor in the long run for BFSI, and can hurt lending and insurance companies in failure of adoption — especially if they ignore ways that could even at the slightest reduce important business metrics NPA, Prediction Accuracy, or Book Size. But like mentioned before, location intelligence may not completely eradicate risk for BFSI, but to directly impact such business metrics that help these industries and their well-intended customers, it is a great start.

Customer ‘Affluence’ cuts close to predict default rates and claim propensity and augment business decisions around it.

About GeoIQ

GeoIQ’s unique software service has harnessed location intelligence to help banks and NBFCs make real-time decisions on incoming borrowers. Our solutions help businesses predict customers upfront. GeoIQ’s affluence index, derived from parameters like income segments, rental rates, commercial activity, population density and infrastructure quality, ranks locations in terms of their affluence. Our solutions help businesses reimagine what they’re up to, on something as simple as a map. To know more about what we do, or to reach out, check out www.geoiq.io

Tusheet Shrivastava is Cofounder and CTO at GeoIQ.io. He has previously helped revolutionize analytics and data science at Global Analytics, HT Media and OnlineTyari. He is reachable at tusheet@geoiq.io

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