How AI is used in the Financial Industrie
Speeded by the pandemic, business is becoming more digital as companies look for ways to gain an advantage over their competitors and modernize their operations for the current environment. Eighty-six percent of companies believe that artificial intelligence (AI) is becoming a mainstream technology in their business, and most expect the use of AI to increase in the coming years. As this trend continues, let's look at how the financial services industry is using AI.
Many financial services firms are benefiting from the powerful capabilities of AI, from increasing efficiency to improving business intelligence. Across the industry, each sector has discovered AI use cases tailored to their specific business needs. As AI technology becomes more advanced and sophisticated, AI in finance will become more common.
How is AI being used in the financial services industry?
The financial services sector is always quick to adopt new technologies, and the integration of AI models and algorithms is no different. Although the type of business and services offered will influence how they integrate AI, there are myriad applications for AI in the financial sector, with relevant use cases expanding all the time.
Examples of AI in finance
From banks to investment firms to insurance companies, AI in finance has proven to be a powerful tool that is transforming the way these businesses operate. For example, companies can identify AI automation opportunities for repetitive and routine tasks that lead to greater efficiency and accuracy. Below, we explore some other important applications of AI in the financial industry.
AI for Loan Underwriting
Credit scoring has traditionally been a labor-intensive and time-consuming process that is not particularly efficient for banks. In order to perform proper due diligence and maintain a healthy risk profile, banks must verify certain information about applicants before granting them credit, including income, assets, property ownership, and outstanding debt. This process requires reviewing various documents, records, data and more, from both internal and external sources - and that takes valuable time.
AI has helped banks make the loan review process more accurate and efficient. AI can process mountains of information faster than humans. Moreover, due diligence and risk management are typically complex processes that help lenders determine success or failure. AI helps banks save valuable time and money by automating and optimizing loan underwriting.
AI for credit scoring
Personal credit score is the most important reference point for financial institutions to assess an individual's creditworthiness. Therefore, the accuracy of a credit score is of utmost importance as companies need to maintain a healthy risk profile for their portfolio. While a traditional credit score is not always updated with the latest information, the use of AI in finance can help banks get a more comprehensive picture of a customer's creditworthiness in real time.
AI models can help assess a customer's credit score more accurately and frequently, which helps banks make better lending decisions. AI algorithms can analyze historical data about an applicant's financial behavior, previous loan applications, outstanding debt, and more to get an accurate picture of how a particular customer affects the company's overall risk profile.
Loan applicants can also benefit from the increasing use of AI in the financial industry. With AI, banks can process more data and information than before to paint a more holistic picture of credit history and creditworthiness. This more thorough review process can give customers who have been disqualified based on their credit score a better chance at getting a loan. This approach is enabled in part by AI models that can analyze alternative data such as employment history or spending patterns.
Using AI for predictive analytics
Being able to predict the future is something all financial services firms would love to be able to do. With increasingly powerful AI capabilities on the horizon, they are one step closer to that reality. Predictive analytics is a powerful tool for all financial services providers, enabling better risk management and more informed credit decisions.
AI models for finance work around the clock to monitor customer behavior and new information in real time, providing actionable insights into likely future behaviors or trends.
By ingesting and analyzing historical data, AI models and algorithms can identify patterns and predict future actions and events. Predictive analytics has important implications for better detecting fraud and accurately determining customer creditworthiness, which improves business intelligence for these organizations and helps them make better decisions.
Being able to predict the future is something all financial services firms would love to be able to do. With increasingly powerful AI capabilities on the horizon, they are one step closer to that reality. Predictive analytics is a powerful tool for all financial services providers, enabling better risk management and more informed credit decisions.
AI models for finance work around the clock to monitor customer behavior and new information in real time, providing actionable insights into likely future behaviors or trends.
By ingesting and analyzing historical data, AI models and algorithms can identify patterns and predict future actions and events. Predictive analytics has important implications for better detecting fraud and accurately determining customer creditworthiness, which improves business intelligence for these organizations and helps them make better decisions.
AI for claims management and fraud detection.
It is estimated that fraudulent claims cost insurance companies around $80 billion annually. Processing the thousands of claims that come in every day is no easy task and quite tedious for the claims handlers who manually review them every day.
Given the sheer volume of insurance claims, it would be impossible for human workers to recognize patterns and quickly determine whether a claim is potentially fraudulent or not.
By using AI in the financial industry for predictive analytics and monitoring new data and claims at all times of the day, AI models are helping insurance companies combat fraudulent claims and improve their detection capabilities. By using natural language processing to analyze logs and machine learning models to identify patterns and flag unusual claims for further review, AI can save companies both time and money in detecting claims fraud.
AI for quantitative trading
With increasing market volatility and growing competition, the stakes are high for today's investors. Moreover, with constant information overload, it's impossible for traders to stay 100% informed and make informed investment decisions through traditional means. Therefore, AI-powered algorithmic trading can help investors execute profitable trades more efficiently.
With AI-powered quantitative trading, investors can better take advantage of arbitrage opportunities that can be easily identified and executed by AI algorithms and models. In today's fast-moving markets, the difference between a profitable and unprofitable trade can sometimes change in milliseconds - a daunting challenge to overcome through human efforts alone. While traders still play an important role in investing, AI models are increasingly being used to help investors stay competitive.
The future of AI in the financial industry
As new technologies and the integration of AI continue to revolutionize the business environment, the financial industry will benefit from the increased efficiency and greater business intelligence that these models can provide. The role of AI in finance is becoming increasingly apparent among companies of all types. Banks, investment firms, and insurance companies are all taking advantage of the benefits AI offers to improve their workflows and operations.
As increased competition in the field and technological advancements continue to impact finance professionals, embracing AI is no longer an option, but a must. With a no-code AI platform like zerocodeai, organizations don't need to learn programming or hire a team of data specialists to implement AI into their operations. zerocodeai users can choose from dozens of pre-built models that best fit their needs, deploy them in minutes, and quickly gain financial insights. Request a free demo today and learn why AI without programming skills is the future of AI in finance.