Understanding and predicting borrower behavior has always played a major role in the lending business. Despite its clinching role, the mortgage industry, has never had a sophisticated mechanism in place to analyze borrower behavior. This, even after the lesson learnt in 2008, when default rates ruined the country’s economy. Credit score —a tool developed in the 1980s—still happens to be the deciding factor in determining the creditworthiness of an individual, thus leaving the industry at the mercy of a faulty and incomplete method of computation.
Why the Methodology is Faulty
Today’s lending methodology still uses the borrower’s age, salary, occupation, etc. to understand consumer behavior. This means two people with the same credit score, are likely to have the same borrowing patterns. If the two people happen be a 23-year old grad and 45-year old employee, painting the two with the same brush, as risky or less-risky borrowers, makes no sense at all.
The one thing that this points to is that the information used to understand borrower behavior is inadequate. To add true value to better decision making, it’s high time to include and analyze some critical factors — borrower’s salary, age, occupation etc. This would mean, including a lot more detailed and finer information — generated internally or purchased from third parties such as social media organizations, taxing authorities, property information portals and the police — and analyzing them to get actionable insights into consumer behavior.
Given that data is being created and consumed at much faster rates and greater variety, what the lending industry needs today is an integrated technology to simplify the data and get an enhanced understanding of borrower behavior.
The Solution
The solution lies in embracing big data – a technology that deploys advanced analytics to analyze influx of real time data. By leveraging this technology, organizations can test suppositions more precisely and get assurances on the integrity of loans.
Big data can tabulate thousands of variables from a multiple sources to correlate with the likelihood of repayment. The variable can be as minute and detailed as use of proper capitalization on the web form or how borrowers use the sliding scales on the company’s online loan application.
The analysis can help lenders create real-time individual risk profiles based on purchase behavior, customer social networking activities, and transaction data. Likewise, it can help in fraud mitigation. For instance, real-time analysis can be undertaken to understand customer credit card behavior over a period of time. Further, petabytes of data can be analyzed in real time to know bespoke customer requirement.
What Organizations Need to Do
To reap the benefits of big data, lending companies need to have a closer look at their systems and processes. Firstly, organizations must streamline their processes for validating and protecting information. Data-visualization technology can help them achieve this to a large extent. Next, companies need to make the data work for them.
Analyzing data from every angles with data mining tools can help companies get valuable and actionable insights from the data. It’s only when these technologies are properly integrated that companies can discern and predict customer behavior with the degree of precision.
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