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How will a bank recognize that it is falling behind in artificial intelligence?

Driven by developments in artificial intelligence and big data, the whole financial industry is undergoing a fundamental change that will become even more pronounced in the coming years. The associated changes entail many opportunities, but also numerous risks. It is already foreseeable that there will be both winners and losers, especially since the degree of maturity of the use of artificial intelligence in banks is very different.
But how can a bank actually notice that it is being left behind from current developments? Here are some scenarios that those banks may experience. The list is subjective and not complete. In addition, there is a risk that countermeasures are no longer possible if the points mentioned below actually materialize.

Increasingly stronger competitors.
Banks can significantly increase their efficiency through the use of AI methods, such as automated text analysis.They can pass this on directly to the customers and offer significantly better conditions. Through intelligent customer analysis, they can also offer tailor-made solutions and react quickly to changing customer requirements.
The other banks will experience that the competition is “unexpectedly” strengthening even in their traditional markets, offering ever-better conditions, exacerbating price pressure and always seeming a bit faster. Despite good economic conditions, this will lead to a loss of profits.

More critical customers.
Customers are becoming increasingly demanding and critical as a result of the aforementioned tailor-made solutions from competitors. What is initially considered an additional feature will become a matter of course over time. As a result, banks will face more and more demanding customers who, in the case of the negative scenario considered, can no longer satisfy them.

Worse risk-return ratio.
Improved early detection procedures – such as text messaging or geographic information – will allow high-credit-mature banks to tailor the risk premium to their actual credit rating.
As a result, customers with poor credit ratings will increasingly migrate to the “suspended” banks. These will face the decision to take disproportionately high risks or operate such conservative lending that little more profits are left.

More frauds.
KI procedures in the area of compliance enable the detection of money laundering, for example, but also internal cases of fraud, e.g. in the retail sector, even in advance. Consequently, there will be a tendency for “fraudulent” individuals to dodge on low-maturity banks. These will correspondingly have more to do with compliance-related issues.

Negative press.
Among other things, AI processes enable the targeted, real-time tracking of reputation and possible reputational risks. Banks that have implemented such procedures can detect such risks at an early stage and take appropriate countermeasures. Reputation-damaging projects tend to be more likely to be carried out by banks with low AI maturity. Accordingly, the focus of journalists – even with regard to the compliance cases mentioned – will shift to these banks.

Thus, it appears that in the coming years, even in the banking industry, there will be extreme competition for the best AI procedures and a “war for talent”.