The financial services sector is always quick to adopt emerging technologies, and integrating AI models and algorithms has been no different. Though the nature of the firm and what service they offer will influence how they integrate AI, there are countless applications for AI in finance, with relevant use cases constantly expanding.
Many financial services firms benefit from AI’s powerful capabilities, from increasing efficiency to enhancing business intelligence. Across the industry, each sector has discovered AI use cases specific to its unique business needs. As AI technology becomes more advanced and sophisticated, AI for finance will become increasingly common.
AI in Finance Use Cases
From banks to investment firms to insurance companies, finance industry AI has proven to be a powerful tool that changes how these firms view their workflows.
For example, companies can identify AI automation opportunities for repetitive and routine tasks that produce increased efficiency and accuracy. Below, we dig into some other significant uses of AI in the finance industry.
AI for Loan Underwriting
Traditionally a labor-intensive and time-consuming process, loan underwriting for banks is not an overly efficient process. To do proper due diligence and maintain a healthy risk profile, banks need to verify certain information about applicants before approving them for a loan, including income, assets, property ownership, and outstanding debt.
This process requires combing through various documents, records, data, and more from both in-house and third-party sources—all of which take valuable time.
AI has helped banks become more accurate and efficient with their loan underwriting process. AI is quicker than humans in processing mountains of information. Additionally, due diligence and risk management are typically complex processes that help lenders determine success or default.
AI is assisting banks to save valuable time and money by automating and optimizing loan underwriting.
AI for Credit Scoring
A personal credit score is the primary marker financial institutions have about a person’s creditworthiness. Thus, the accuracy of a credit score is of utmost importance, as firms need to maintain a healthy risk profile for their portfolio. While a traditional credit score isn’t always updated with the most recent information, using AI in finance can help banks gain a real-time and more comprehensive picture of a customer’s creditworthiness.
AI models can help score a customer’s credit accurately and more frequently, helping banks make better credit decisions. AI algorithms can analyze historical data on an applicant’s financial behavior, previous loan applications, outstanding debt, and more to accurately picture how a particular customer impacts the firm’s overall risk profile.
Loans applicants can also benefit from the increased use of AI in the finance industry. With AI, banks can process more data and information than before to paint a more holistic credit history and creditworthiness picture. This more thorough-vetting process may allow customers that were disqualified due to their credit scores to have a better chance of securing a loan. This approach is partially enabled by AI models that can analyze alternative data, like employment history or spending patterns.
Using AI for Predictive Analysis
Predicting the future is something that all financial services firms wish they could do. With more powerful AI capabilities on the horizon, they are one step closer to this reality. Predictive analysis is a powerful tool for all financial services firms that helps with better risk management and more thoroughly-informed credit decisions.
Financial AI models work around the clock to provide real-time monitoring of customer behaviors and emerging information to provide actionable insights on probable future behaviors or trends.
By taking in and analyzing historical data, AI models and algorithms can recognize patterns and forecast future actions and occurrences. With important implications for better fraud detection and accurately determining customer creditworthiness, predictive analysis improves business intelligence for these firms and helps them perform better decision-making.
Claims Management and Fraud Detection AI
It’s estimated that fraudulent claims cost insurance companies around $80 billion annually.
Processing the thousands of claims that come through each day is no easy task and is quite tedious for the claims processors who manually review them each day. Considering the sheer volume of insurance claims produced, it would be impossible for human workers to detect patterns and quickly determine when a claim is potentially fraudulent or not.
Utilizing AI in finance for its predictive analytics capabilities and monitoring emerging data and claims made at all hours of the day, AI models are helping insurance firms combat fraudulent claims and bolster their detection capabilities.
By leveraging natural language processing to evaluate transcripts, and machine learning models to detect patterns and flag unusual claims for further review, AI can save firms both time and money for claims fraud detection.
AI for Quantitative Trading
With increasing market volatility and growing competition, stakes are high for today’s investors. Plus, with the constant flow of information, it’s impossible for traders to stay 100% up-to-date on the latest markets and make informed investment decisions through traditional means. Therefore, AI-based algorithmic trading can help investors execute profitable trades more efficiently.
With AI-powered quantitative trading, investors can better take advantage of arbitrage opportunities, which can be easily detected and acted upon by AI algorithms and models.
In today’s quick-moving markets, the difference between a profitable and unprofitable trade can sometimes change in milliseconds—a formidable challenge to execute through human efforts alone. Even though traders still play a significant role in investing, AI models are increasingly used to help investors stay competitive.
The Future of AI in Finance
As emerging technology and AI integration continue to revolutionize the business environment, the finance space will benefit from the increased efficiencies and more significant business intelligence that these models can provide. The role of AI in finance is becoming more clear among firms of all types. Banks, investment firms, and insurance companies are all taking advantage of how AI can enhance their workflows and operations.
As rising competition in the space and technological advancements continue to impact financial professionals, it is no longer a choice for them to embrace AI—it’s essential.
With the Accern NoCodeNLP Platform, firms do not need to learn how to code or hire a team of data specialists to bring NLP into their operations. Citizen data scientists, such as analysts and researchers can dozens of ready-made NLP models that best match their needs, deploy them in minutes, and quickly extract financial insights.
Schedule a demo to learn more about the Accern NoCodeNLP Platform and how you can extract fast and accurate insights from unstructured data for investment decisions.