More than 60 years have passed since artificial intelligence was a daring concept at Dartmouth Сollege which only got half of the requested funding. Right now, AI is a $9.5 billion industry, projected to reach $118.6 billion by 2025, according to
Due to its immediate applications in streamlining processes, improving customer care, and managing risks, it has been widely adopted by the frontrunners of the financial industry. From NLP to replace front desk and call center employees to robots analyzing
transactions and loans, there is a way to use machine learning in the banking and payment sector.
Let’s take a look at the most common ways AI is revolutionizing the finance sector right now.
Better Risk Evaluation
Powered by machine learning, AI is excellent at finding patterns in data. Credit companies have heaps of information regarding past customers, ranging from demographic and sociographic insights to payment schedule adherence. It follows that these tools can
show the risk of default with high accuracy.
This kind of solutions could be extrapolated and used to assess the potential risk posed by lenders who have no previous credit history, like college graduates and immigrants, based on other traits which include them in a specific risk cohort. With such
decision-making tools in place, the underwriting process can be highly automated, cutting the costs and processing time significantly.
More types of risk are credit card fraud and the risk of default regarding credit card payments. Based on statistical models, the machine can flag those customers with a high risk of making late payments.
The same models can be adapted to fraud prevention, comparing usual spending patterns and geolocations to attempted payments in the present.
Not only the consumer market can benefit from
AI in finance, but the B2B options can also make a significant difference in the trading arena. These algorithms can be trained to perform automatic trading based on defined hedging rules. They can make predictions, buy or sell on the spot, or place future
orders based on trend estimations.
Personalized Customer Care
The finance industry employs a great deal of support personnel for back-office and customer care roles. These jobs are highly repetitive, require attention to detail, and imply high stress levels that in their turn cause high employee turnover.
At least some of these jobs could be soon replaced by process automation bots and customer-facing chat bots. This doesn’t mean that call centers will be wiped out, just that their number will be reduced, and human agents will be free to deal with non-standard
issues. All the other repetitive issues will be handled by machines using natural language processing, which will allow them to answer in a human-like manner.
Banking chat bots can not only act as 24/7 personal clerks, but they can also become financial advisors, helping people save more money, be on time with payments, and manage risky behavior such as over-drafting their accounts.
The usual self-help features including checking the balance, paying bills, and adding recurrent payments can be delegated to smart assistants since these are better executed by a machine than a clerk during office hours. As the robot processes these operations,
it learns from them and can even come up with suggestions on how to save more money or which expenditures could be reduced.
Automated Trading Platforms
The rise and fall of the cryptocurrency market brought to worldwide attention the use of high-frequency trading robots. These are built on quantitative trading models which analyze the best trading decisions and zero in on the patterns to replicate them.
The advantage of using big data here is that these patterns can be retrieved from various interconnected data sets, as well as unstructured yet relevant data, such as news and press releases. Some companies also include SEC filings or similar government-required
data in this analysis to determine the best stock prices.
The good news is that such tools operate almost in real time and can assist brokers in validating their decisions. This is achieved by combining on-the-spot data storage solutions with forecasting apps.
As mentioned earlier, the financial industry uses a lot of additional services, which usually translate into heaps of documents to be verified, classified, indexed, and stored. AI can also help with these tasks.
Identity theft is one of the major concerns of any banking or financial application. Tools like Confirm.io (acquired by Facebook) can confirm the identity and the validity of documents, thus saving customers a trip to their nearest bank. This is done with
fine-tuned image recognition.
There is also a growing number of algorithms focused on the compliance and management of legal documents. These include features for error checking, comparing versions, and allowing multiple editors to make changes, as well as those enabling fairness and
In places not affected by the GDPR, it is easier to create tools for fast background checks, which is just one step of the credit approval process. These could use interlink databases like criminal records and driving fines and evaluate risks on the spot.
These also use natural language processing to scan for potential misuse cases.
Finance is a sector that is a rather late adopter of new technologies due to regulatory and compliance requirements; yet it is also one highly interested in cutting costs. This puts AI companies in the position of having a harder time to enter this market.
However, this market offers potentially high payoffs once the tech goes mainstream.
The current best use case scenarios for AI in finance are dedicated to the consumer, B2B and trading sectors. While in the B2C area the focus is on personalization, cutting costs and preventing fraud, the B2B sphere is dominated by risk management, ensuring
compliance and looking for more ways to automate tasks.