3 Applications of finance AI

Published on 11/03/2021 by Toby Cox

Artificial intelligence (AI) in finance can cut costs, improve efficiency, and transform how your business manages its money. 

Finance AI

The finance industry was an early adopter of financial technology (or fintech) such as artificial intelligence (AI), and its popularity among financial institutions continues to rise. According to Gartner research, AI and machine learning (ML) tools rank as the top “game-changing” technology in financial services (full research available to clients). 

AI can automate routine tasks, increasing process efficiency, and use machine learning, deep learning, predictive analytics, and natural language processing for more robust features such as chatbots and robo-advisors. According to a Business Insider report, 80% of banks are highly aware of the benefits AI presents to financial institutions.

Adding AI to their fintech repertoire can help banks and financial institutions improve their customer experience, reduce costs, and increase revenue. 

How can banks and financial institutions use AI? 

Let’s discuss some of the ways that AI is being applied to finance services in order to provide more value to customers, while still saving money. 

Use intelligent automation to save time and money

By automating routine tasks, financial institutions can save time and money, minimise errors, and collect data along the way. 

According to Gartner research, 66% of finance leaders expect to focus more on automation in 2021 and also predict that goals related to automation will be the most difficult to achieve (full research available to clients). 

AI software can be applied to different finance operations and processes (full research available to clients). Here is how:

How AI can help in expense management: An AI solution can read receipts and categorise them based on a list of accepted expense types or vendors pre-loaded in the system. Employees will still need to review expenses that the system rejects. 

How AI can help in accounts payableAI can extract and compile data from PDF invoices allowing teams to focus on more complex tasks.

How AI can help in regulation compliance: AI-powered tools can use natural language processing and ML to scan documents for certain terms that would show compliance with regulation standards such as GDPR.

Using AI to automate processes doesn’t replace workers, but rather gives them time to focus on more complex tasks. 

Use predictive analytics to inform decisions

According to Gartner, most finance teams spend nearly half their time gathering and cross-checking information to create reports and forecast predictions (full research available to clients). AI can save teams time, make reliable forecasts, and reduce the possibility of error. 

For example, AI and ML can predict payment habits of customers. If a customer is deemed likely to pay late due to past behavior, the business can remind them of their payment much earlier than they would with customers that pay on time. This is called a ML-Improved A/R (accounts receivable) process. 

Machine learning can predict payment habits

This particular approach helped Iron Mountain, a storage and information management service company, reduce their turnaround time to settle invoices by 40% (full research available to clients).

AI and predictive analytics can also help financial institutions assess and manage risk – 42% of banks and investment services either already do use or plan to use AI for risk management (full research available to clients). It can also reduce the risks associated with giving loans to clients and also improve fraud detection. 

Personalise the customer experience 

By using AI, financial organisations can gain more powerful insights into customer satisfaction and can personalise the customer experience. 

For example, instead of relying solely on a person’s credit score, banks can use AI-powered solutions to consider other factors of a person’s financial history, such as their repayment habits and how many loans they are currently paying. This information can be used to customise the individual’s interest rate. 

AI can also help clients manage their portfolios more effectively. This can be done through robo-advising and digital wealth management, both of which are on the rise. To illustrate, Axyon AI uses deep learning to create investment strategies, allocate assets, and flag market inconsistencies (full research available to clients). 

AI forecasts investments
Axyon AI’s dashboard forecasts what users can expect from investments (Source)

AI tools can save your enterprise money and your employees time, and using AI-powered tools such as chatbots, conversational AI, robo-advisors, and analytics can also help you improve your customers’ experience with your business.

Trust remains the biggest challenge to finance AI implementation 

Despite the benefits of using AI in financial services, some leaders might hesitate. According to Gartner, the two biggest factors deterring organisations from implementing AI technology are fear of the unknown related to risks with AI and not knowing how to start (full research available to clients).

To mitigate the risks associated with AI, you should consider whether the AI you’re adopting is easy to understand or explainable. Additionally, it’s important that the AI is not kept in a “black box,” available to only data scientists and developers (full research available to clients). 

The need for explainable AI

You can also implement training programs to educate staff on AI risks. Be sure to adhere to data privacy standards and adopt security measures into your AI operations. 

If you’re new to AI or looking to replace current options, check out our catalogue

Methodology

*The Gartner Top CFO Priorities for 2021 survey was conducted in October 2020 among Gartner for Finance Leaders members and other CFOs. Qualified respondents are the most senior leaders in the finance function (CFOs). The total sample is 173 respondents, with representation from various geographies, industries and sectors. The survey was developed collaboratively by Gartner’s research data and analytics team as well as expert researchers. 

Note: The applications selected in this article are examples to show a feature in context, and are not intended as endorsements or recommendations, obtained from sources believed to be reliable at the time of publication.

This article may refer to products, programs or services that are not available in your country, or that may be restricted under the laws or regulations of your country. We suggest that you consult the software provider directly for information regarding product availability and compliance with local laws.

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