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AI in the Finance Industry


Whilst Artificial Intelligence (AI) and its applications such as machine learning are disrupting several industries, one of them that has benefitted the most is financial services. The reason why we are seeing such huge strides in the sector is due to it being incredibly traditional and, in some cases, quite old-fashioned. For example, we have banked in the same way for a long time but as people are so digitally connected, this is not necessarily the way to carry on doing things. This article looks at some of the major innovations within financial services over the last few years.

Credit Decisions

Using AI, credit scoring can be more sophisticated than traditional methods. The aim to is use data that can distinguish between applicants who are high risk and those who simply don’t have a detailed credit history. Some establishments have even tinkered with using social data to gauge a measure of credit worthiness. For example, if somebody has a lot of secure connections with family and friends they can trust, it has shown to be a strong signal they are a good risk. Ford Motor Company has shown that machine learning is able to more accurately predict the risk of thin-file applicants. Beyond this, AI is not biased like a human might be. The decisions on credit will be based purely on the data it processes without human judgment of any kind. A more rational decision ensures enhanced risk management.


AI can analyse vast amounts of data almost in real-time. The advent of cloud computing and the recent deployment of 5G technology will only extend the availability of such services. In analysing a lot of data quickly, multiple sources can be used, whether they be structured or unstructured and help to make decisions. For example, they can predict the future risk of an applicant. US leasing company, Crest Financial have successful deployed real-time machine learning to reduce the lag in making risk-based decisions.

Fraud Prevention

Fraud is and always will be a big problem within financial services. AI has been very influential in combatting this type of crime. Whenever new technology is integrated, as much as it is great for the business, it gives those looking to cause malicious damage a new channel to explore. Think “Catch Me If You Can” style to get an idea of what can happen in the extremes. Using machine learning, algorithms can track user behaviour and spot patterns that seem irregular to the norm. Increasing e-commerce transactions have made this more difficult than ever and AI is becoming fundamental in combatting cybercrime. In another big area of criminal activity, money laundering, banks have reported that AI can reduce the time of investigations by as much as 20%. As consumers become more connected, there is an opportunity to reduce digital based crimes as much as there is to cause them.


Data driven investments and automatic trading are becoming big business and reinventing the industry. There are several benefits from using AI in the stock markets over human advisors, albeit both still have a part to play. With the capacity to process and analyse large volumes of data, trading algorithms are perfectly placed to help investors make the right decisions. Real-time data processing means fast decisions and fast trading. It’s a win/win situation for everybody involved. AI makes recommendations purely based on the information it is provided with. This means there is no bias in its decisions. Humans can easily be influenced by their own “gut feel” but this is not the case for machines which are primed to make the perfect unemotional decision. Huge companies like Bloomberg now use AI for their forecasting and market predictions. They can identify patterns quickly and accurately for traders.


Whether it be checking your balance, scheduling bills or making payments, banking is becoming heavily driven by technology and AI. Most people will have a banking app and the number of occasions where somebody needs to call or visit a physical bank have massively decreased. I’m not even sure a Millennial would know what to do with a cheque! AI applications within mobile apps have the ability to create financial goals and automate customer savings. They do this through tracking income and expenditure to create fully optimised plans for their customers. Banking has been crying out to be more streamlined for years and with the promise of Open Banking which we are slowly seeing creep into society, the sector is still set for a lot of re-development in the coming years.

Automating Processes

AI has reduced resource costs within financial services. Think about applying for a loan. Traditionally, you’d have to fill in various forms, sign documents, send photographic evidence for ID and maybe even pay your bank a visit. Machine learning algorithms can take this structured and unstructured data, process it in seconds and make a decision. There doesn’t need to be a human at the other end reviewing all the documentation. Instead, they can focus on customer care. Financial services require a lot of repetitive and mundane tasks. Ernst & Young have reported up to 70% cost reduction in automating these tasks. Most of this is from removing the human involvement and deploying those staff elsewhere. JP Morgan Chase have started to successfully deploy Robotic Process Automation (RPA) as a way to better capture documents and automate cash management tasks.


Financial services is continually being reshaped by AI. New technology like blockchain and adoption of cryptocurrency over the next decade look to have the potential to drastically change the landscape again. Whilst some are slow to adopt AI applications with the cost of investment, those who do deploy new solutions are starting to reap the benefits. Data protection and privacy are also one of the major obstacles when attempting to deploy AI and that is something that firms within the sector will have to battle to overcome. Much of that will come from consumer trust and understanding over time. The AI hype is real and financial services is amongst one of the most highly investable sectors for it.