Financial risk is an inherent part of the world of financial institutions. Improper or poor risk management can have serious repercussions for the customers.
The fallout of improperly managed risks also has the potential to impact the financial institution’s reputation adversely. Following the 2008 meltdown, the financial sector has become much more circumspect and risk-averse, and rightly so.
The sector started putting more stringent checks and balances in place, with the active application of vigorous risk management mechanisms because of the lessons we learned from Frank and Greenspan. The advent, and subsequent progress of artificial intelligence technology in the past decade, has proved to be a huge boon for the finance industry.
The financial sector seems poised for sustained growth as it learns to leverage AI for risk management. Let us find out the common risks intrinsic to the financial sector, and artificial intelligence technology’s role in overcoming them.
Loan Defaults and Credit Risks
Every year, banks and financial institutions suffer heavy losses due to credit or loan defaults. Admittedly, forwarding loans or allowing credit is a financial activity with some inherent risks.
But loan default is generally a result of an improper or uninformed assessment of default histories and credit scores. Unavailability of intuitively predictive models results in poor credit assessment and subsequent write-offs.
With AI, banks can determine the creditworthiness of credit card or loan applications by using customer data to build predictive models. The insights gained from machine learning models can help banks to spot probable defaulters and curtail write-offs.
Some fast movers among the financial institutions are pioneering the use of AI analytics to cut down on the credit risk. Their default rates are already down by up to 20 percent.
Risk Assessment with AI and ‘Alternative Data’
Predicting the probability of future default by an individual is vital for finance companies. At the same time, knowing an individual’s repayment capacity bears a direct relation to the loan amount.
Gathering correct and complete information for these tasks is sometimes fraught with complexities due to the wrong information furnished by individuals and businesses. More companies are now relying on AI instead of potentially erroneous manual processes for complex risk analysis.
Traditionally, companies have used parameters such as income and FICO scores to decide if an individual or a business qualifies for a loan. But with the latest tools and technologies at their disposal, they can now pore over ‘alternative data’.
‘Alternative Data’ includes a potential borrower’s entire life, including their extensive digital footprint. Industry sources believe this data can provide an insight into borrowers with verified FICO scores, and those with no credit history.
Stock Market Investment Risks
Stock markets have always been the most volatile, unpredictable, and risk-prone areas of investments in the financial world. As global economies get more intermeshed, there are too many micro and macro factors that can shape the market trends.
It is becoming harder to accurately predict the rise and fall of the stock indices with a manual assessment of the trends. Financial companies and banks, wanting to maximize returns with stock market investments, face these inherent risks.
Risk Mitigation with Data Modeling and Sentiment Analysis
AI tools like time-series data modeling are helping to forecast market trends. Algorithms and big-data analytics can identify complex patterns and help portfolio managers make informed investment decisions to minimize risks.
Machine learning researchers are also looking to tap into news feeds and social posts to analyze general market sentiments. This could help them in predicting market trends.
Uninformed and poor decisions by the management of the financial institutions and banks have the potential to put their investments at risk. Significant exposure to risk by the management can jeopardize the interests of public and private stakeholders.
Sometimes, poor internal operations and management decisions can prove to be catastrophic for the organization. There are examples such as the 2008 collapse of RBS, or the Lehman Brothers debacle.
Managerial Insights with Machine Learning
AI modeling can now help financial institutions with operational insights. These valuable insights combined with advanced data analytics and machine learning technology can equip businesses to make informed managerial decisions.
Fraudulent Transactions Risk
Fraudulent transactions using stolen customer information, expose banks and insurance companies to financial risks. Financial institutions need safeguards against frauds involving stolen identities and cards.
Every year, hundreds of thousands of credit card fraud cases result in huge losses to the banks. On the other hand, fraudulent claims are responsible for piling on huge losses to insurance companies.
Use of AI to Flag Fraud
Highly developed and advanced AI applications are capable of flagging and blocking suspicious ATM or online banking transactions. Banks are now investing in artificial technology products that can detect fraudulent transactions with up to ninety percent accuracy rates.
Some of the big banks now use biometrics and AI-based applications to authenticate transactions and customer information. These applications help protect customer identity and prevent both, the bank, and the customer, from potential fraud.
Similarly, insurance companies are also using more sophisticated classification models, based on machine learning and artificial intelligence technologies, to identify and block any suspicious claims with greater success than before.
AI and Insurance Underwriting
Underwriting of an insurance policy involves an appraisal of the applicant’s risk profile. An insurer also needs to calculate the probability of an applicant’s propensity to seek a claim under the policy.
Both the policy premium payable by the insured, and plan benefits accruing to them, are based upon the underwriting process. Any neglect or mistake in underwriting could lead to losses for the insurance company.
There is a growing use of artificial intelligence in the underwriting process by insurers. AI is making it easier to assess data to classify the risk, and calculate premiums and plans suitably.
Adapt and be Nimble
Financial institutions, banks, and insurers are embracing artificial intelligence and machine learning applications to manage risks successfully. Constant evolution and rapid growth in AI mean that the finance companies need to constantly innovate and adapt to eliminate risks.
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