9 Exciting Applications of AI in Finance

Finance is a broad sector, but the potential use applications of AI are even broader still. AI has the potential to reduce and better manage risk, automate manual processes, improve the data used to make decisions, and enhance customer service.

The following nine applications of AI are among the most exciting developments currently taking place in the finance sector.

1. Financial Planning & Analysis

In the financial sector (as in other industries), Financial Planning & Analysis (FP&A) teams play an essential role. Their job is essentially to receive and assess data from multiple sources – qualitative and quantitative data – to make decisions and then report their findings up the chain. The information they provide helps senior executives make strategic decisions concerning the direction of the company.

AI can improve the processes of the FP&A function in a financial institution in a number of ways. First, the increasing volumes of data available to FP&A teams require solutions to ensure all available data is properly processed. AI is an alternative to increasing staffing resources.

In addition, AI technologies like Robotic Process Automation (RPA) and predictive analytics can automate repetitive processes and improve the accuracy of models, reports, and recommendations.

2. Assessing Credit Risk

AI technologies can give financial institutions access to additional data sources to help them improve their processes of assessing risk.

One potential use case is assessing the credit risk of an individual with minimal credit history, as the AI tool can use other factors in addition to the factors they currently use, such as assessing the payment history on previous loan accounts.

Financial institutions can also use AI tools to assess their overall credit risk. For example, machine learning and predictive analytics technologies can look at macro factors such as house price indexes, rates of inflation, or unemployment statistics.

With this information, the system could constantly re-evaluate the organisation’s risk exposure, make predictions about future default levels, and automatically adjust underwriting processes to re-balance the company’s risk position.

3. Fraud Detection

Banks and other financial institutions already commonly use AI technologies for fraud detection and anti-money laundering. These applications are typically designed to detect and red flag anomalies, i.e., transactions that are different from an accepted normal baseline.

The problem is the anomaly detection approach returns a lot of false positives, flagging legitimate transactions as fraud. Machine learning and predictive analytics can help to reduce these false positives by giving the algorithm additional data points for deciding whether a transaction triggered as an anomaly is indeed fraudulent.

4. Trading and Investments

There are several ways that AI can help improve trading and investment processes, particularly concerning decision making:

Natural language processing (NLP) technologies can be designed to gather data from across the internet to look for investment opportunities. These AI tools can look for information on merger news, acquisition news, financial information, company news, customer sentiment, etc.
Predictive analytics tools can also help with trading and investment decision-making and processes. Specifically, predictive analytics can help with the selection of stocks that will deliver the best return on investment. This technology could also be used to choose the best time to open or close a position, set an optimum investment level, etc.
Machine learning tools can be used to create risk profiles of individual stocks and commodities, as well as to help build overall investment portfolios based on client risk profiles.

5. Underwriting

Efficient and effective underwriting is based on access to high-quality data. Crucial to the process is assessing risk, and for that, you need accurate and informative data. AI tools and technologies make it possible for financial institutions to get access to new data streams that will help them improve underwriting decisions.

6. Optimising Insurance Claims Processing

AI technologies can automate many of the steps involved in processing an insurance claim. In some situations, a customer could make a claim just by interacting with a chatbot.

Predictive analytics and machine learning tools can also be used by the insurance industry to ensure that any pay-out they are making on a claim is in line with pay-outs in similar situations. It will also be possible to use machine vision technologies to assess damage using images and videos before paying out on a claim.

7. Sales and Customer Service

The potential of chatbots in the financial industry is something many executives recognise, particularly in relation to answering customer service queries. They also have applications in sales where a chatbot can help customers choose the right product before processing the sale, although currently, this typically only applies to simpler products.

8. Digitising Historical Files and Documents

Digitising historical documents and files is a significant undertaking, but machine vision software automates the process, improving speed and accuracy. Not only does the software create a digital image of the document in PDF form, but it can also read and understand the contents of the document, turning that content into data that can then be used for other purposes.

9. Improving Document Search Processes

Some of the AI applications mentioned above are being used today, some are in an early form, while others will be possible in the near future. One thing is for certain, however: AI is transforming the financial sector and has the potential to deliver substantial benefits.

The Tesseract Academy can support you in the journey of AI adoption through our long-term support programs, as well as our workshops. Also make sure to check out some our other free tools, as well as our insights videos.