AI in Finance: Money Making Machines

In 2016, long before the mainstream popularization of AI, JP Morgan Chase was looking for ways to boost their capabilities as a firm. Turning to AI, they launched a platform Contract Intelligence (COiN) that used image recognition technology to compare and match different clauses as well as patterns with terms and locations in contracts. This simple task of interpreting business contracts and agreements saved about 360,000 hours annually. JP Morgan also created an AI research team with an emphasis on research that seeks out ways to improve COiN’s current algorithms to provide better predictive capabilities. Today, the bank has invested over $2 billion in building AI centers and developed IndexGPT, a software that tailors select securities to customers’ needs.

According to the Cambridge Center of Alternative Finance, 85% of financial service providers are currently using AI with 95%  expecting to be using the technology to generate new revenue in two years. Finance has always been an industry at the forefront of new technology, especially those that help with fraud prevention and risk management and AI is not an exception. Banks and other financial service providers are using the technology for sales and credit analysis, customer service, risk management, client acquisition and many other services.

AI in risk management

In the financial services industry, risk management is one of the most important aspects of any endeavor. It is the process of identifying, analyzing and treating threats, risks and uncertainty in financial decisions that may result in loss of the company’s capital or operations. This helps to proactively eliminate or reduce any potential dangers that could harm a company negatively. By introducing AI into this process, companies are able to use existing data to make predictions about potential vulnerabilities.

One of the most common uses of AI in risk management is with fraud detection and prevention. With the use of AI-powered algorithms, data mining, database analysis and anomaly detection techniques, risk management professionals can identify fraudulent activities and transactions. AI is also used in threat intelligence analysis especially for aggregating and analyzing intelligence data and trends to train ML models. This is useful in recognizing patterns of events as they happen. These models can also be used to analyze data related to the risk of workplace injury or accidents before they happen, making sure that all bases are covered.

AI in customer service

In previous years, customer service used to depend solely on hundreds of human agents in call centers manually working through each customer’s task or need. Today, with the use of AI, a lot of that process is being automated freeing up human resources for more complex tasks. In finance, customer service is a little more delicate but integrating it with AI is proving to be a game changer. According to The Economist, 80% of customers expect AI to improve customer service and 43% of financial services firms are using AI to automate their customer service experience. With the aid of AI, customer service departments can set up intelligent call centers that allow customers to connect with chatbots.

Integrating AI  in customer service also gives financial services firms a unique opportunity to analyze data gotten through audio transcriptions or text conversations to improve or adjust their customer service experience. With this addition to the CS department, firms can automate processes like reminder and marketing emails, as well as alerts and notifications. Clients will also be able to access personalized recommendations for products and services and even investments. This will help reduce the time and cost spent reaching clients while allowing customers to access information and services that are relevant to them.

AI in predictions and forecasting 

The financial services industry is run by predictive analytics and forecasts. Fraud detection, risk management, lending and a host of other activities depend solely on the ability of a firm to forecast future outcomes. With predictive analytics, financial firms are able to determine market trends and anticipate their customers’ behavior while taking into account market fluctuations and future events. The addition of AI to this very important task allows firms to explore all the ways that the technology can be used to improve forecasting and decision making. This is as the predictive analytics sector continues to grow with the global predictive analytics market projected to grow from $14 billion in 2023 to over $65 billion by 2030.

The use of AI in predictive analysis is continuously evolving but the finance sector currently uses three main types of predictive analysis models;

• The Classification Model that uses straightforward modeling to produce a binary output. It can be used to predict the performance of a company’s share price

• The Outliers model that identifies significant deviations in a data set, making it perfect for fraud detection,

• and The Time Series Model that tracks a variable over a specified time frame to understand how it will be affected in another time frame. 

All of these models are used in one way or another to predict a wide variety of events such as the probability that a customer will make late payments. They are also used in credit risk management, budgeting, risk management, revenue forecasting and other finance processes.

AI in client acquisition 

AI is likely to bring $1.4 to $2.6 trillion of value for global marketing and sales and the finance industry is prepared to take advantage of this. With the help of ML algorithms and generative models, marketing teams are able to reach different audiences with personalized customer journeys and content tailored specifically to their preferences and needs. Firms also have access to advanced analytics tools that can be used to identify insights into their customer’s behavior across various platforms. 

According to a survey by the University of Cambridge, 69% of leading financial institutions have used AI for client acquisition, and for good reason. AI powered chatbots alone can increase customer engagement rates by up to 400% and integrating AI with customer acquisition combines the best that technology has to offer with human creativity. This helps to increase the effectiveness and efficiency in lead generation, reduce customer acquisition cost, and save firms money and resources.

Conclusion

The use of AI in the financial services industry is a huge contributor to the continued growth of the industry with the banking market worldwide projected to grow by 4% between 2024 to 2029. The industry’s ability to innovate and embrace new technologies is obvious in all the ways that AI is being used to improve and automate various financial processes. From predictive models that predict how stock prices will change over time to fully automated call centers that personalize interactions with customers, the industry is fully embracing AI as a revolutionary tool.