Artificial intelligence (AI) is being used by companies all over the world to automate various processes, but there’s particular potential to go further in the area of finance. Businesses can use the technology to police transactions and detect fraud in the banking and investing arena.
Not only does shifting these traditionally manual operations free up employees’ time, but it also cuts out the possibility of human error. Saving money on paying for staff hours and preventing fraud could help corporations to cut their outlay by millions every year.
1. AI for fraud detection
Fraud costs credit card companies more than $24 billion a year, putting detection high up the list of issues that need tackling. AI does this through the analysis of inputs, including a customer’s past transactions, their home location and shopping habits to create a neural network that can accurately predict whether transactions are legitimate or not.
The largest private bank in Turkey, İşbank, has wide-reaching plans to use AI for fraud detection. These include developing robo-advisers, image recognition technology, AI forecasting and estimation software for investment. Burak Arik, CEO of Maxitech, the innovation arm of İşbank, said: "We can see it has the potential to make huge savings."
2. AI to mitigate risk
Risk is a major theme in finance and comes in a number of forms, from an investor’s appetite for risk to compliance and cybersecurity risks. All of these areas can benefit from AI as a way to mitigate the risk, which is why it’s gaining traction in the world of banking. Rules-based risk management systems are no longer the industry standard, because they have a number of weaknesses that can’t be ignored.
While supervisory organizations oversee compliance in financial institutions, these businesses can police themselves through AI-based software. It allows these firms to more accurately identify the rules that apply to them and work within them to ensure they’re not left open to fines or other penalties.
3. AI to create more accurate forecasts
Accurate forecasting in both the long and short-term is vitally important for finance companies and their budget and planning cycles. Without comprehensive revenue projections, it becomes difficult to invest efficiently, which can lead to poor financial performance and missing targets. That’s why AI must be deployed at scale throughout the sector, with the better business outcomes ensuring companies don’t miss out on revenue.
AI forecast methodology must be intelligent, agile and reflect market dynamics, which was only achievable in controlled conditions just a few years ago. Now, it can take structured and unstructured data from both internal and external sources to create the most accurate projections the industry has ever seen. As time goes on, the system only improves as it learns from any inconsistency between forecasts and the final numbers.
4. AI to avoid taxation errors
Cognitive, machine and robotic learning processes can be incredibly useful when it comes to taxation. With more tax authorities demanding transparency across jurisdictions and determined approaches to management and collection, there’s increased pressure on businesses. This has the potential to expand the workload exponentially unless AI is brought in to aggregate, validate and report to authorities.
It won’t just be companies that are deploying AI either, as tax authorities and advisors will turn to the technology too. Commonly asked questions and frequently encountered issues will be dealt with by AI, which alongside data analytics is set to transform the taxation system in many parts of the world.
5. AI itself must be policed
While AI has the potential to police various parts of the financial industry, it’s vital that its very methodology isn’t overlooked, as it creates personally identifiable information. Data governance must be taken into consideration as larger amounts of data are joined in an unprecedented fashion and presented in new forms, shapes and sizes.
Now that data can be pulled together so easily to identify patterns and intersections, privacy is a key concern. Of course, there are regulations in many jurisdictions to help ensure institutions are taking care of this potential issue, but in many ways it’s up to individual organizations to ensure they’re keeping such information safe.
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