Every business experiences some uncertainty as they forecast – that’s why they often come up with three scenarios; the ideal, the most likely, and the worst case. But with AI predictive modeling, companies can remove many of the unknowns in their future. Simudyne, for example, enables financial institutions to stress test at a large scale using AI. As technology evolves, the use of AI in financial services is growing more and more important. And it’s imperative for companies not to get left behind.
Find out more about the opportunities of AI, and see how organizations like Trustpair use machine learning to prevent payment fraud. Request a demo to learn more!
How is AI used in finance?
Although artificial intelligence models have risen in popularity in just the recent years, AI has actually been leveraged in financial services for at least the last decade. Fortunately, the strict regulation and transparency rules in financial services has protected the public against some of the more general potential risks of AI.
For example, one of the concerns over ChatGPT is that we can’t see how it makes its decisions – the algorithm is hidden (in a digital black box). But that’s not going to fly in finance and banking – as transparency, even in proprietary technology, will be key to achieving regulatory approval.
Here are some examples of use cases for AI in finance:
- Transaction categorization
- Suspicious data detection and response
- Personalized product recommendations
- APIs to automate
Transaction categorization
Most consumers experience automated transaction categorization these days in their banking applications, and especially if they use budgeting apps. The AI here uses natural language processing (NLP) to draw analytics information from the transaction code, and translate this into one of several possible spending categories.
For example, imagine the transaction: Shell $44 shows up. The program would draw the word “shell” from the transaction, knowing that this means gas station. From there, it’s easy to categorize the transaction into “transport”.
Suspicious data detection
By constantly monitoring all of the incoming and outgoing transactions on an account, AI programs can find patterns in spending or savings. This makes it easier to detect suspicious behavior that can indicate fraud risks and react accordingly.
For example, Trustpair uses a machine-learning algorithm to detect data changes and activity. Where a vendor submits a change in payment details, for example, Trustpair can assess this against the most accurate global databases to verify whether it is indeed the vendor changing their bank details. Or, for example, whether a fraudster has hacked into their email account and submitted their own payment information instead.
What’s more, once suspicious activity has been detected, Trustpair can automatically block the payments from exiting your account to theirs. This process uses machine learning to ensure your business is protected against payment fraud.
Learn more about online payment fraud prevention here!
Personalized product recommendations
Onto something a little less sinister now; improving your conversion rate.
One aspect of business is product offers – and in financial services, these aren’t traditionally thought out. In the past, banks and credit unions have been known to bombard their human customers with credit card offers, loans, new savings accounts, investment opportunities and more. Not only can it be overwhelming, but also confusing for the consumer, leading to few conversions.
But machine learning models can be applied to see where the customer is at in their financial situation. For example, determine whether they’ve lost their income (no regular salary transactions incoming for the last three months), and might require help with a remortgage offer. Alternatively, banks could view that your customer has received a raise, has bought plane tickets for their next vacation, and might want to open a credit card with travel rewards.
By relying on AI to assess the situation, your advisors will gain better insight into the consumers’ situation and can provide the right product offer at the right time. This intentional strategy should help increase conversions across the market, as Yapily has proved.
APIs to automate
One of the most obviously beneficial use cases for AI solutions in financial services is that it can speed up data entry. This helps improve efficiency across the whole business, from supplier management in Procurement to customer service.
For example, loan applicants could authorize lenders to access their tax information through an API. By opening a secure gateway to a business’ exact revenue, expenses and profit or loss figures, for example, the workload is reduced for the business loan applicant. Plus, the lender knows that this information is as accurate as it gets – leaving no room for typos or other errors.
Finance automation with AI is making decisions quicker and more efficient. They benefit the consumer directly, and save on precious resources for the institutions, since it prevents any back and forth.
What are the advantages of AI in finance?
AI models have transformed the landscape of financial services in the last decade, and the innovation is likely to continue. But even as we sit at the beginning phase in of AI in financial services, there are notable advantages:
- Accuracy
- Speed
- Flexibility
Accuracy
Automation through AI eliminates the possibility of manual human errors. This digital transformation makes the accuracy of data much higher, and leads to higher confidence in decisions made based on this data.
One of the reasons why AI platforms are so accurate is due to granularity. This term describes the level of detail of your information.
Take the example of transaction categorization from earlier; some financial institutions rely on merchant category codes (MCCs), which are not always present in transaction data. By delving into multiple levels of data instead, AI programs can see the context around their transactions to categorize them more accurately.
Here’s an example transaction: Shell – $1.50
Level 1 granularity might read shell and categorize into gas stations and transport.
But level 2 granularity takes the price into consideration, nobody is buying only $1.50 of gas. This must be categorized as a convenience store purchase.
It’s clear that with more granularity, generative AI can be used to increase the accuracy and management of decisions in financial services.
Speed
Even better, each of these decisions happen almost instantaneously, since AI systems are programmed to make decisions based on conditions. By removing the emotional element, the programs make approval or denial decisions based on whether conditional logic is satisfied.
Moreover, since AI programs in the bank industry use APIs to connect various third parties, the customer also benefits from a faster and more convenient process.
For example, businesses can automate their expense report with a platform that’s connected to their bank. Platforms like CoreIntegrator use a robotic process automation to automatically identify invoice data, and add descriptions and product codes. They can even match each line of a purchase order to each line of their invoice, to be sure that vendors are delivering all of the products they should.
By doing this automatically, AI expense platforms significantly reduce the time for month-end reconciliation. This frees up accountants and finance professionals to work on value-adding tasks that can really impact the efficiency of the business, doubling down on the benefits.
Flexibility
Finally AI systems in financial services offers businesses more flexibility than ever. There are different types of artificial intelligence softwares, and companies can opt for a centralized or decentralized model.
Centralized AI refers to a small team of AI experts that build programs based on their own expertise. The creators are very clued into artificial intelligence and therefore apply best practices, but might not develop their platforms with the business’ exact products in mind. Traditional banks, like the Bank of America, are more likely to use a centralized system.
Alternatively, decentralized AI platforms tend to be built with input from product, sales, marketing and other functions of the business. But with this comes longer lead times (as there’s more feedback to consider), and could risk missing out on industry best practices. Fintechs like the Emma app use a decentralized platform, powered by Yapily.
The beauty of these two approaches is that each organization can determine the right method for them – bringing lots of flexibility as financial services opt for AI.
What are the risks for financial services?
Fraud
The biggest risk of AI in financial services is fraud. Even without AI, this represents one of the largest threats to fintech today, and extra algorithms, software and technology can further complicate the process. Right now, since AI is in its infancy, we don’t fully understand where the vulnerabilities lie.
So it’s a race to detect any points of exploitation before the fraudsters do, and before they use AI themselves to commit fraud.
One way to prevent access from fraudsters is to protect your processes, rather than technologies. For example, Trustpair’s vendor fraud prevention platform automatically monitors the queue for outgoing payments and alerts your team when account errors or anomalies arise. No matter which AI platforms you may use for the payment itself, your financial accounts would be protected.
Learn more about AI-based fraud in our latest fraud report!
Unexplained decision-making
Another big challenge associated with all AI technologies (not only in financial services) is the ‘black box’ associated with decision-making. Machine learning algorithms are built to evolve over time, as they get access to more and more information. But without transparency over how those decisions are made, the integrity of AI technology can be called into question.
It’s looking like this problem will be solved as regulatory requirements are introduced around AI in critical sectors, like finance and healthcare. But until that happens, it’s up to business leaders to do their due diligence when it comes to how AI programs reach their decisions.
What is the future of AI in financial services?
As mentioned, it’s looking like we’ll see some incoming regulation around the uses, limitations and requirements of AI in financial services.
In fact, Europe is seeing a headstart as the EU recently announced its Artificial Intelligence Act, which will be enforced from September 2024. This regulation will categorize platforms based on their risk level, and is the first of its kind.
It’s likely that the US will follow suit as they strive to lead global innovation and keep a lid on the risks of artificial intelligence.
Either way, as the capabilities of AI evolve, so will the risks to financial services. So protecting their business, processes and customers with platforms like Trustpair will become more important than ever.
