How Alternative Data is Changing the Credit Risk Modeling Landscape?

Many Indians struggle to get a loan because of insufficient credit history and the absence of a credit score. This is because credit scoring models have primarily considered traditional data to derive the creditworthiness of a customer or a business. By traditional data, it means the data from a lender’s own payment history, credit utilization, credit history, types of credit, and new credit. This creditworthiness is formed into a 3 digit number called a consumer credit score that attempts to measure the person’s credit payment pattern. All this is achieved using traditional data.

However, as mentioned before, a considerable population does not have a credit history, and are practically invisible to the credit bureaus – hence they fall into the category of “Credit Invisibles”. For calculating the creditworthiness of such people, there is a need to consider what is called ‘Alternative Data’. Alternative data is any data that is not directly related to a consumer’s credit behavior. This data can be derived using a number of unconventional sources. Using this data, credit risk management models can develop a credit score so that lenders can safely extend credit to an entirely new customer base, and consumers and small businesses can get access to the credit they deserve.

Where can we find Alternative Data?
There are various sources of alternative data that can be used for credit risk assessment to build a credible personality of a consumer. Let us take a look at the apparent few.

  1. Retailer/ Purchase data: In today’s times of retail malls and online transactions, much of the customer data is recorded in detail. This data is best to provide behavioral insights. Purchases detailed on a loyalty card, for instance, would give a fair idea of a particular family income and structure. However, this data is hard to retrieve outside marketing purposes as retailers do not generally sell such information to credit bureaus. But also, using certain tools, this data can still be accessed, albeit anonymously without personal information using only details such as banking transactions, browser cookies, and device IDs.
  2. Social Media & Web Pages: Your LinkedIn profile can tell about your employment history and professional connections. Your Instagram and Facebook profiles can indicate a lot about your lifestyle choices and spending habits. Although, not all information portrayed on social media can be taken on its face value. The value of this data can be understood to be far lower than the value of data with a stronger credit connection.  How a customer navigates through your website, how long they stay and where they click can be predictive of various parameters too.
  3. Telecom, Rent, and Utility bills: Rental payments, electricity bills, water/gas bills, telephone bills, and all such periodic payments are among the most reliable alternative data sources. Monthly payments provide an accountable history of consistent behavior and financial responsibility. Telecom & electricity bills can reveal multiple traits about a customer such as full name, billing address, payments due and payments done, etc. All these points reveal whether the customer pays their dues on time and in full or otherwise.

    Use of Machine Learning to Extract Useful Information
    While it is true that there can be enough data sources, a mechanism like Machine learning will surely be required to extract only sensible & usable information from the sea of data. With large, unstructured data sets, the right use of ML & AI can help identify data patterns that relate specifically to credit risk.

    The credit industry now needs to acknowledge the importance of financial inclusion and the lending gap it needs to close. Credit information companies have started seeking credit scoring model that include alternative data to establish a credit score. According to market insight, there are an estimated 3 billion adults worldwide who don’t have credit records. Tapping that market should be a priority for lenders.

The Importance of Credit Risk Management in Banking

Banking operations come with the factor of risk; it’s inevitable. In the simplest way possible, risk is an uncertainty of a situation or event that may happen in the future and for banks, it’s the uncertainty of an outcome of business investments. The various types of banking risks may be classified as Strategic risk, Compliance risk, Credit risk, Cyber Security risk, Liquidity risk, Market risk, Operational risk, etc. Out of these ‘Credit Risk’ represents the most important type of risk for commercial banks.

Credit risk is understood simply as the risk a bank takes while lending out money to borrowers. They might default and fail to repay the dues in time and these results in losses to the bank. Loan portfolio management is very important but most times a bank can’t fully assess if it will retrieve the money back because even if the borrowers have been paying their dues on time, the economy might show shift and change the way things have always been. So, what do banks do then? They need to manage their credit risks.

The goal of credit risk management in banks is to maintain credit risk exposure within proper and acceptable parameters. It is the practice of mitigating losses by understanding the adequacy of a bank’s capital and loan loss reserves at any given time. For this, banks not only need to manage the entire portfolio but also individual credits.

How do banks set up a Credit Risk Management system?

Even though every bank may have their own approach to establishing credit risk management models, there are a few basic steps that every Credit Risk Management includes-

• A complete understanding of a bank’s own capital reserve.
• Understanding a bank’s overall credit risk based on individual, customer and portfolio levels.
• Implementing an integrated and quantitative credit risk solution to make an appropriate credit risk environment.
• The business model in place should be such that is ever-evolving, able to achieve real-time scoring to limit monitoring, have data visualization capabilities and business intelligence tools to make it available any time.
• Establishing a sound credit-granting process or criteria that will clearly indicate the bank’s target market. This should include appropriate credit administration, measurement and monitoring process.

These are some principle ways to set up a Credit Risk Management system that will help in minimizing risk and maximizing reputation and productivity. Often, banks do prefer having a consulting agency to look after their Credit Risk Management since managing credit risk is a tricky task due to a lot of recommendations and predictions, thus there shouldn’t be any possibility of loopholes in the process.

What are the advantages and disadvantages of Credit Risk Management?

The advantages-
• It helps in predicting and/ or measuring the risk factor of any transaction.
• It helps in planning ahead with strategies to tackle a negative outcome.
• It helps in setting up credit models which can act as a valuable tool to determine the level of risk while lending.

The disadvantages-
• Prediction is not entirely scientific, so judgement made can go either way.
• Cost and control of operating a credit scoring system are questionable.
• While different models may work, there are no guarantees. For this reason, some banks prefer ‘one model

Finally, …
Whether you’re trying to manage risk at your own company or you’re just trying to manage your credit, the study of credit risk management provides a framework for understanding the true nature of credit risk present in your organization. While profitability is a consideration, credit risk management is about seeing beyond profitability, and more precisely to help the CEO and CFO to develop a quantifiable sixth sense about operational cash flow.