Machine learning and Artificial Intelligence are to transform the functioning of the financial services industry. One of the critical functions of a lending institution is to assess the obligor’s creditworthiness because the future business of these entities depends on the ability of the borrowers to repay. Hence, the risk management department implements varied methods to assess the probability of repayment – and this assessment has to be done for every person the money is lent. But, the current methods employed by banks and NBFCs are not good enough – to say they are outdated. With AI and ML changing the world at an unprecedented speed, it’s time that lending changes. So how can ML help us with the analysis of credit? Let’s find out.
The current method of credit assessment
Financial Institutions lend based on credit scoring models that they have developed. Firstly, credit data of potential customers is obtained from outsourced credit agencies or through other means like surveys and questionnaires. The data is then put into the credit model that uses regression and analysis to predict the consumer’s future behaviour. Now, there is an issue with this method. These models are statistical models, which means that significant statistical assumptions accompany them.
Moreover, these models are backwards-looking; the analysis will happen after considering only the historic data and not the borrower’s current situation. So, after putting in hours of effort in predicting the customers’ future repayment capability, there is a high chance that the organisation could go wrong. And the world has witnessed so many instances of high-sum NPAs, all to whom lenders extended the credit after undertaking the credit analysis procedure. So, how can AI be used in finance?
AI and ML to better credit decisions
There have been significant advancements in two spheres of the process –
1) Obtaining data
2) Predicting the behaviour of consumers using models
Alternative data credit is increasingly being used to source data from new roots. For instance, using modern-day techniques, lending organisations collect behavioural data from online platforms – to illustrate – what drives the consumer’s impulse buying decisions and what decisions impact the consumer’s spending habits. Lenders can then combine the newfound data with traditional data sources to better analyse credit decisions. Using Artificial Intelligence and Machine Learning, entities can develop predictive models with better data connections and analyse multi-dimensional data.
Significant advantages of alternative data credit: The future
1) Accurate predictions
The accuracy of predictions sets the ball for better underwriting. Every borrower is living a different story and is impacted by different factors. It is pivotal to identify and pinpoint negative news that could potentially impact an individual borrower – and this comes under the new-age data that lender’s don’t usually track. When this data is combined with the traditional kind of data available with the institutions, lenders can make better business decisions.
2) Newer models
Machine Learning has made analysing a wide array of data convenient. Newer models are being created that gather, collate and update the data in real-time. Technology has the potential to identify patterns that are typically missed by the human eye – patterns that can significantly impact the quality of lending decisions and the business in turn.
3) Quality data analysis to increase product offerings
So they say, “Data is the new gold.” – couldn’t be more accurate. Once a financial institution has access to the historical data of its customers, it can use it to predict future behaviour and spending habits. Let’s say an organisation knows during which time of the year a customer’s spendings peaks. The entity can then pitch an additional line of credit to the customer during that period, tailored to suit the individual’s needs.
4) Target new customers
Once a financial institution starts considering data points other than the usual for analysing customers’ behaviour, it could bring more people within the ambit. It would be possible to target a segment for whom significant data wasn’t available earlier. With better credit approvals and money flowing to society, the economy as a whole also prospers.
Automation gets the tedious work done faster and increases the accuracy of the results. Once the credit scoring model is entered into the system, the approval and sanction can be executed within minutes with today’s technology.
AI and ML are entirely on track to transform the lending and financial service space.
The risks associated with AI and ML
Though big data is the future, the industry is currently trying to mitigate certain risks. The biggest is the reliability and accuracy of data collected, which results directly from the data collection methods implemented. Another concern is data protection and security. There is a heightened reliance on computers and the web to process, store and analyse data, which leaves the organisation susceptible to online frauds, viruses and data theft. No business owner would compromise on data security; hence the expenditure on web safety has increased over the years. Governments are phasing out regulations to protect users’ privacy, all in all supporting the budding businesses.
AI typically refers to a machine or a programme taking human decisions. Now, for this to be possible, a huge dataset is needed. As this is a newer segment in technology, analysis on such a large scale has hardly been done before. Hence, it is crucial to avoid any data gathering errors. The data sources of alternative data credit and traditional models are different, making it essential to better the quality of data obtained from the new sources. If the data is not accurate, the entire decision-making process could be compromised, leading to wastefulness.
Moreover, if the data entered into the system is biased, the entire algorithm could be biased. A biased algorithm could lead to lending based on religion and gender preferences, which would harm the business and the economy.
Financial Institutions are shifting to AI and MI gradually, completely disrupting the traditional modes of operations. Those who implement it accurately and swiftly win the race to the top – but there are several challenges along the way. It is crucial to look out for regulations and adapt to better credit decision making.