AI & Machine Learning in Risk Management

Warren Buffet, the CEO of Berkshire Hathaway and a great philanthropist, once said: “Risk comes from not knowing what you are doing”. In other words, more information is helpful in minimizing risk and thereby lead to better decision making.  Financial institutions such as banks and insurance companies collect a lot of data to know more about their customers and products. Thanks to artificial intelligence (AI) and machine learning (ML), we have better risk management tools now as compared to traditional analysis. The largest insurance companies and banks are adopting AI and ML technologies. New ML applications are developed for the credit risk and insurance risk management.

Information is helpful in minimizing risk and thereby lead to better decision making

Insurance companies and banks expose themselves to financial risks. In general, risks are classified into two types: a) Pure risks such as a risk of loss from fire or theft. Here the possibilities are a loss or no loss. There is no gain possible. b) Speculative risks where either gains or losses are possible. Product risk, investment risk, or brand risk are some examples of speculative risks. Businesses use third parties such as insurance companies and banks to minimize their risks. Banks and insurance companies, therefore, have financial risks such as default risk, interest rate fluctuation risk, risks from mortalities, and natural disasters etc.

Traditional risk management methods are outdated. When a lender (or insurance provider) receives applications for loans from businesses, they have to evaluate the applications based on the measures of profitability and leverage. Traditional methods such as manual calculations or predictions based on FICO score do not provide useful or accurate predictions. Thus, a bank may end up with more risk than it desired. To avoid such a scenario, the financial industry is looking for more accurate risk management solutions. Furthermore, regulatory authorities are requiring banks to move away from inefficient legacy systems to accurate and automated model risk management systems.

Big Data and ML-based risk management have opened up the possibility of including complex models in the analysis to assess risk. ML algorithms for risk management are used to meet various objectives such as such as regulatory needs, business goals, and shaping institutional risk culture. ML algorithms have a distinct advantage that it can identify patterns from the data without any rules-based programming. For instance, in assessing credit risk, ML algorithms take into account many factors such as loan repayment behaviors, liquidity ratio or similar financial information, and high-risk demographics. A classical model assumes a linear relationship between credit risk and the factors affecting it. On the other hand, ML algorithms are able to spot non-linear relationships by using methods such as random forest predictions or boosting. The accurate risk modeling based on machine learning and AI results in four distinct benefits:

Precise pricing – e.g., to monitor pricing of various products such as insurance premiums
Improve operations – e.g., provide digital solutions for asset management or wealth management
Loss prevention – e.g., applying machine learning algorithms for assessing credit risks
Analyze risk characteristics – e.g., natural language understanding NLU analyzes data from unconventional sources such as yelp comments, SEC filings to analyze risk.


High complexity of ML algorithms requires sophisticated model risk management

Apart from these advantages, there are some pitfalls that banks and insurance companies need to be aware of. One of the major hurdles, that comes with machine learning algorithms and increasing complexity, is ‘model risk’. To build sophisticated and efficient models, model risk management (MRM) is necessary.  It is critical to focus on the transparency of algorithm design or else automated systems become “black boxes” vulnerable to biases and errors. Next, increased complexity and weak or lack of governance may lead to ML algorithms that are not credible. These pitfalls are avoidable by having a competent advanced data analyst team on board. Some companies collaborate with an external data science company to overcome these pitfalls.

Fintech or financial technology solutions such as AI and ML are bringing in a revolution in the financial sector. In 2017, companies that have invested in fintech and insurtech related projects are expecting 20% ROI. This has set up fintech projects to exhibit a tremendous growth rate. The time to incorporate fintech (in particular AI, ML, and advance analytics capabilities) in the workflow of your company has arrived.

September 12, 2017

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