This blog post provides an overview of how financial lenders are using Artificial Intelligence (AI), Machine Learning (ML) and Natural Language Processing (NLP) for counterparty and credit risk analysis.

Financial lenders need to understand the risk that potential borrowers may have on defaulting or being late on payments. Hence, they monitor the borrower’s credibility, identify any early warning signals that show deteriorating credit, and evaluate the potential impact a given change in credit standing can have on the borrower. In addition, financial lenders use the insights to design new business models including marketing and pricing strategies.

Five Key Factors in Credit Risk

Financial institutions use the 5 Cs of Credit to evaluate risk: credit history, capacity to repay, capital, the loan conditions, and associated collateral. Structured and unstructured text data are researched and analyzed to identify each of these Cs from sources, such as credit rating bureaus, news, and public information. The 5 C's of Credit Fly Wheel

Credit Risk Assessment Challenges

Manually finding the right insights from the vast amounts of unstructured text data from a large set of sources creates the following challenges.

  • Time-consuming and labor-intensive: Credit analysts must spend hours and sometimes even days and weeks manually monitoring the credit activities of all existing counterparts and key service providers.
  • Real-time monitoring: It is often challenging to identify events in a timely manner and understand the potential impacts before detrimental effects have been realized in the market.
  • Not enough information: There is often a lack of information for small medium-sized businesses. In contrast, for public companies, there is often too much information to analyze across too many sources. Therefore, critical information can be missed in the assessment process.
  • Analysis: Analysis, usually, is based on the use of static sources such as credit ratings and annual financial statements.
  • Incomplete data: Attempting to combine traditional information (public, financial, corporate, among others) with alternative data such as key market events and company news without a standardized, automated process.

Challenge of Analyzing Unstructured Data for Credit Risk

Today, 80% of data is unstructured and is projected to grow at 55-65 percent each year, creating more obstacles for analysts to uncover the right insights. The main challenges of identifying credit risk by researching large sets of unstructured data include:

  • Manual, labor-intensive, and time-consuming processes involved in monitoring large sets of data for existing counterparties
  • Inability to identify real-time events in a timely manner
  • Lack of critical information on small to medium-sized businesse
  • Too much information on public companies

How Banks and Lenders Navigate Around Unstructured Data

Traditionally, financial institutions rely on analysts and data scientists to perform manual research and analysis to obtain the right insights.

Monitor News

Since news is constantly changing, credit analysts must actively monitor the credibility of securities, companies, or individuals to determine the likelihood that a borrower can repay their financial obligations in real-time.

Analysts review the borrower’s credit and financial history to determine whether the subject’s financial health and economic outlook are positive.

Interpret Financial Statements

When analyzing the potential borrower’s behavior and credit history, analysts 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, it could indicate a decline in revenue and potential future bankruptcy, which may affect the bank’s assets, ratings, and reputation.

Simulate Impact

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.

Using AI/ML/NLP for Credit Risk Management

Banks and lenders can leverage AI/ML/NLP to monitor data in real-time from news sources, social media, financial news, and more. The fast and accurate insights gained from AI natural language processing (NLP) can lead to revealing early indicators of potentially detrimental events to individual companies, sectors, or regions. The indicators can be prioritized by relevance and enable users to highlight potential risks.

Ai/ML/NLP solutions deliver benefits to the front, middle, and back-office operations of a bank by:

  • Reducing the time to credit decisions by automating credit risk assessment processes.
  • Improving the prediction power of existing credit risk models or easily designing new credit risk models.
  • Reducing credit loss by recognizing emerging risks and potential downgrades early on.
  • Providing customers with tailor-made products or solutions based on their level of risk.
  • Obtaining hidden insights from private companies or SMEs.
  • Improving the deployment and utilization of risk and assurance resources.

Accern NoCodeNLP Platform

The Accern NoCodeNLP Platform enables credit risk analysts and data teams to use pre-built workflows or can build custom use cases to monitor and gauge a borrower’s risk. Using no-code NLP, analysts can quickly and accurately identify and quantify early warning indicators of a company’s deteriorating fiscal health.

The Accern pre-integrated data store provides a plug-and-play aspect so users can quickly identify credit events from global and local news sources, corporate filings, company transcripts, investment research and more about a business entity. Users can then assign a credit sentiment score based on the strength of adverse credit signals.

Credit Signals

Users can run real-time use cases, which monitor news, financial filings, and news on specific companies in real-time. With NLP models such as sentiment analysis, adverse content can be identified for a specific company, industry, or region based on bankruptcies, default, debt restructuring, rating downgrades, and more.

Based on the insights gained, signals can be created to help analysts further identify areas of potential risk and distressed companies.

Early Warning Indicators

By combing the credit signals with the correct market data, users can forecast the potential credit deterioration before a significant credit event occurs. Lenders can stay on top of counterparty and credit risk at all times, without the intense manual research and monitoring by analysts.

Research Efficiency

Pre-built NLP models cut down significantly on analyst time spent on company research and monitoring activities for both public and SMEs. Using the intuitive interface, analysts and data teams can quickly filter credit signals and deploy adaptive NLP models to classify entity, theme, and documents followed by analyis of sentiment and relevance within the data. This enables data teams to eliminate 99% of the noise found in data and improve their search results for specific content.

Schedule a demo to learn about the Accern NoCodeNLP Platform.