Artificial intelligence (AI) and financial services have only formed a coherent whole for a handful of years. Yet, the role of machine learning and AI-based recommendation has become central to how the finance industry approaches revenue, sales, marketing, security and customer satisfaction.
The main reason for this shift in perspective is the emergence of well-adapted tools that allow banks and other actors to harness the full potential of this technology. One such tool is Explainable AI, which bridges the gap between AI and financial services by providing a completely transparent and compliant solution to assist in decision-making processes. Machine learning and algorithm-based technologies are just as promising. But, how did we get there, and how much can artificial intelligence currently do for financial services?
The brick-and-mortar model, which has been the standard for banking all over the world since its very inception, started to undergo a revolution in the 1980s. Of course, back then, AI and financial services were not concepts that went hand in hand, as the former was little more than a wild dream. Yet, barely a decade later, financial institutions began to offer their customers something that would forever change the age-old model: Internet banking.
By the mid-2000s, the service had become a new standard for over 80% of banks worldwide. In a context where individuals now fully embrace dematerialized services, fintech is picking up the pace and AI and financial services have come together to meet their need for connected banking.
As stated above, customer service is one of the driving forces behind the development of AI-based technologies in banking. Thanks to chatbots and artificial intelligence, building trust and loyalty is facilitated by the fact that feedback is received and processed on a constant basis. Machine learning allows financial actors to utilize raw data in ways that were never even conceivable before to improve customer experience based on concrete evidence. By analyzing all the information available AI provides insights that more traditional survey methods simply could not match.
Working conjointly, AI and financial services have increased security, both for the end consumer and for the banks themselves. Fraud and anti-money laundering processes benefit from algorithm-based solutions considerably. Having tools that can analyze the history of risk cases, determine early signs of failures and study real-time activity to issue relevant warnings promotes great efficiency. Verification procedures and transaction monitoring are also facilitated by machine learning. Similarly, risk management is handled by AI-based credit scoring which utilizes much more sophisticated and complex rules than manual verification ever allowed. Loan applications are therefore processed automatically and without the need for tedious cross-referencing between unending piles of papers. Instead, more attention can be given to pre-accepted applications.
Preventing information leaks and cyber attacks falls under strict regulatory requirements that banks have to comply with. Such compliance is greatly simplified by automation, which allows critical information to transit efficiently and without the risk of human error.
AI and financial services have only brushed the surface of what they can accomplish together, and the future will no doubt prove just how many more processes can be facilitated, and how much customer service can improve. On the horizon is increased account and transactional security, faster, cheaper and more reliable transactions, highly efficient digital assistants to help with advanced personal finance management and more changes made possible by automation. As banks and customers alike continue to embrace the advances of artificial intelligence, we will certainly experience incremental improvements on the banking offer and more streamlined functionalities.