Traditionally, financial institutions such as commercial and investment banks, credit card institutions, credit rating agencies, and investment firms have relied on analysts to perform manual processes to understand credit risk and optimize lending decisions. But, with the tremendous increase in unstructured text data, the banking industry faces obstacles in manually monitoring risk in real-time.
In this article, let’s look at how AI is being increasingly used in banking to automate time-consuming manual processes.
Unstructured Data Challenge in Banking
Credit analysts may actively monitor the credibility of securities, companies, or individuals to determine the likelihood that a borrower can repay their financial obligations. Analysts will review the borrower’s credit and financial history to determine whether the subject’s financial health and economic outlook are positive.
For example, an analyst at a bank may examine a restaurant’s financial statements and credit history to identify its cash flows and ability to pay on time before approving a loan for new kitchen equipment.
When analyzing the potential borrower’s behavior and credit history, analysts will interpret financial statements to determine whether the borrower has an abundant cash flow and good credit standings. This is called early mitigation as analysts try to identify early warning signals of any signs of deteriorating credit.
For example, if a business client is struggling to pay its bills on time, this could be an indicator of declining revenue and future bankruptcy, which may affect the bank’s assets, ratings, and reputation.
Credit analysts can also run a simulation to understand the potential impact a change in credit can have on the borrower. Economic changes caused by the environment, stock market volatility, legislative changes, and regulatory requirements are a few of the risk factors that analysts will keep in mind. After considering risk factors, analysts may recommend specific courses of action for the borrower to take such as suggesting a business loan or business credit card.
If the bank proceeds with the loan approval, the credit analyst will continue to monitor the borrower’s performance to ensure that banks have the correct information to act fast and mitigate risk. Based on what the analyst interprets, he or she may issue recommendations such as reducing the loan or credit limit, switching to a new credit card, or even terminating the loan agreement. Determining the level of risk on a loan, credit card, or investment helps banks manage risk.
Data Analytics in Banking
To have a holistic understanding of risk, analysts must build their views from both traditional unstructured sources such as brokerage reports, news and corporate filings, as well as non-traditional sources such as trade journals, social media, blogs, call recordings and earnings/corporate transcripts.
As unstructured data continues to grow at 55-65 percent each year, commercial and investment banks, credit card institutions, credit rating agencies, and investment firms face challenges in sorting through and extracting key insights from the large volume of data and are increasingly adopting AI into their business processes.
Applying Natural Language Processing to Unstructured Data
Natural language processing (NLP) is one of the fastest growing technologies in AI that enables computers to understand text and audio data in the same way that humans can- with the human language. By applying NLP to unstructured text data, lending institutions, investment banks, and credit agencies can reduce costs in operations across the front, middle, and back offices.
The wealth of information NLP uncovers can offer banks a competitive advantage through insights into customers’ lives, goals, needs, and challenges. A few of the main areas where NLP insights can be applied are described below.
Adverse Media Screening
The complexity, sophistication, and scale of financial crimes has rapidly increased over the past decade. Banks and financial institutions use adverse media screening, such as know your client (KYC) checks and anti-money laundering (AML) to enforce anti-crime measures.
Banks can manage risk by using NLP to gather data in real-time about a customer or prospect, including any negative information about them. Data analytics can provide information from the open web, deep web, and other premium sources that include structured and unstructured text data.
Credit Lending Processes
Banks can use NLP to extract data from borrowers’ behavior online and analyze people’s searches, location and payment data to determine creditworthiness. For example. the market for real estate lending is massive with over 10 million homes and commercial properties selling each year. According to the Fed’s latest report, mortgage debt from homes and businesses tops $15 trillion. Banks can mitigate risk by analyzing the potential borrower’s behavior and credit history, analysts will interpret financial statements to determine whether the borrower has an abundant cash flow and good credit standings.
Insights for Coverage Bankers
Investment banks are realizing the value of NLP in providing clients with personalized recommendations and deepening customer relationships. Coverage bankers must know the historical and real-time updates of a specific market industry in order to provide unique investment insights to corporations and governments. Through NLP and automation, coverage bankers can quickly uncover industry-specific trends, corporate news and events, and identify the best financial deals.
To manage risk, analysts can evaluate the probability of default (PD) and early warning signals in credit risk. Traditionally, PD and early warning systems require extensive research, analysis, and expert judgment to identify indicators of risk. NLP can help analysts identify patterns and monitor indicators from a wide range of sources to generate early warning signals on potential credit migrations, payment supplier risks, negative news on public and private companies, analyst ratings, and more.
Insights gained from NLP allow banks to enhance adverse media screening and credit lending processes, gain insights into deteriorating credit, and manage risk.
Case Study: Automating the Credit Risk Process with Accern NoCodeNLP Platform
Fundomate, an SMB funding company used the Accern NoCodeNLP Platform to fully automate the credit risk process. From data collection to infrastructure and support, Accern supported and helped customize the Fundomate AI process to improve predictions on credit default and manage risk.
With the Accern NoCodeNLP Platform, banks can quickly deploy credit risk solutions including out-of-box workflows to enhance a variety of functions – credit risk, anti-money laundering and more.
While analysts can use these pre-built models immediately, data scientists can customize and retrain these models to their specific needs.enables financial analysts to use NLP models without having to code. Banks can implement NLP easily and quickly to enhance efficiency and productivity across the back, middle, and front office value chain.
Schedule a demo to learn more about the Accern NoCodeNLP Platform and how it can help drive ROI for your private equity investment portfolio.