Big data analytics based on artificial intelligence (AI) models can play a major role in improving credit scoring and providing higher security to lenders and protection to borrowers.
The credit business in the US
With almost $3.8 trillion in debt recorded in September 2017, credit lending is a big business in the US. The challenge posed by this volume is steadily growing beyond the capabilities of traditional credit-scoring models, even ones as stable and effective as the FICO.
When we add another layer of derivative financial instruments on top of the base lending assets, the need for as much precision as possible assumes even more importance.
Determining creditworthiness the traditional way
Traditionally, creditworthiness has been based on analysis of three aspects – the individual (assets, collateral, cashflow and credit history), future inflation predictions, and the expected future growth of the economy.
The basic information to be assessed for determining creditworthiness includes past borrowing and repayment patterns. Here are two reasons that illustrate why credit scoring needs to expand beyond the direct analysis of raw data from historical records.
First, an individual may not have a credit history at all, which should not preclude them from being viewed as creditworthy, but often does. Second, a past default may make an individual an unattractive choice to lend to. However, there may be an entirely reasonable explanation, such as being laid off at work.
In both scenarios, big data analytics could completely change the financial depiction of the applicant, as we will see later in the article.
What is big data analytics?
Big data analytics is essentially gathering and analysing data on a large scale from multiple sources to identify emerging patterns. These patterns can then be built into predictive models that increase statistical accuracy and reduce human errors.
Big data analytics can couple algorithmic credit-scoring models with cashflow/finance histories and social media data to enable lenders to make decisions that may otherwise not be an option with traditional credit-scoring models.
FinEX Asia believes that incorporating big data analytics into creditworthiness assessments can also significantly reduce risk margins in high-yield asset classes.
How does big data analytics apply to lending?
Such advanced analytical models have huge potential in the world of finance and creditworthiness.
Lending is a business based entirely on calculated prediction. Lenders try to use borrowers’ information in an attempt to identify whether or not they are at risk of defaulting on their loan repayments.
Big data solutions could assimilate raw data records from thousands of credit bureaus and lending companies to clearly identify situational and behavioural patterns, and these can help define new and completely objective parameters for choosing the right candidates to lend to.
Let’s consider an example
Imagine a scenario where individuals in a certain job or industry need to relocate frequently. Lenders typically see such recurrent movements as a sign of instability and may be wary of extending credit to such persons.
With a big data analysis, a pattern of higher attrition could be assigned to that particular job or industry. Here’s how it helps lenders: if a borrower has not defaulted in the immediate past, and belongs to the said industry at the time of the loan application, the lender knows that the loan-seeker could be at risk of defaulting and can therefore make a decision accordingly.
The example above is a typical predictive model at play – a scenario where the applicant has not yet defaulted and may have a clear, strong credit history. However, given the general trend in the specific kind of job and the match with a frequent change in the residential address, the applicant fits into a clear, high-probability default pattern.
Predictive vs historical analysis
This ability to predict future credit behaviours is where big data-based AI models can leverage existing models such as the FICO.
The factors used by FICO – payment history, amount owed, length of credit history, new credit, credit mix – are all based on transactional, historical data. Thus, while FICO does provide some insights into an individual’s potential default risk, AI models using big data could illuminate this far better. As illustrated in the earlier example, this predictive element can help us draw a better overall picture of a borrower’s credit behaviour.
Let’s look at a possible scenario where borrowers in a certain segment of society keep borrowing to pay off older loans. In case of such a scenario, it would once be observed and recorded that for a long time, the implication would be the access to the new borrowing method, which uses big data analytics and can flag such risky borrowing behaviour.
The ability to analyse vast amounts of data to understand the link between situational and behavioural data to identify patterns between the two will help to predict the probability of defaults with a much lower margin of error.
Given its apparent strength, FinEX Asia applies AI-based models to ensure that credit scoring is done with the least margin of error, in the knowledge that such applied learning systems benefit both the borrower and the lender. It protects lenders by offering an opportunity to detect potential future defaulters, while also increasing the opportunities for worthy borrowers to avail themselves of credit at fair interest rates.
To learn more about FinEX Asia’s consumer credit funds, register today.