Machine learning, language processing, robotics - the artificial intelligence toolbox offers a range of smart instruments that enable machines to act autonomously. The list of destinations in the digitalisation journey is almost limitless, making it crucial for financial institutions to have a clear and focused direction. This enables them to keep pace with the industry’s rapid changes.

Banks face upheavals in process automation, in the first wave of automation, 20 to 30 years ago, they saw how certain processes were removed from branches and transferred to new centralized units. In these units, processes were serialised and trimmed for productivity. With digitalisation and AI, many processes can now be fully automated for the first time. This promises both significant cost advantages and faster processing and thus a better customer experience.

For banks to be successful with digitalisation and the use of AI, a number of prerequisites must be met.

1. Digitalisation requires new skills

New technologies always come with new skill requirements. The management of digital processes and automated decisions requires employees to look at a process the way they would look at a production line – to think about error tolerances, measurement techniques and when to manually intervene with a case. While there’s feedback from human employees about the success or failure of actions, this kind of proactivity can't be expected from a machine.

2. Digitalisation requires agile methods

Banks that are successful with digital processes monitor them closely and adjust continuously. To facilitate a continuous learning loop, banks implement a cross-functional strategy team that reviews the results of the individual process steps jointly with the respective business owners and develop ideas for improvements and test them.

The prerequisite for this agile and quantitative approach is the ability to implement adjustments quickly and largely without the involvement of IT resources. Therefore, the decisioning technology used must enable employees without programming skills to configure rules and communication content independently.

3: AI and operations research unleash additional effectiveness

Which credit offers or limits a customer receives, which marketing initiatives are promising for a target group or how intensively receivables management approaches customers – the decision strategies that guide these actions are generally developed based on business experience. Analytics are frequently used to segment the portfolio — but analytical methods are often underleveraged when assigning decisions, measures or prices to these segments. This leads to considerable opportunity costs. 

I expect that methods from operations research will be increasingly used in the development of price strategies, but also when developing targeted operational processes  – which in some industries, such as manufacturing, has been common practice for decades. With mathematical decision optimization, the dependencies between turnover, profitability and risk goals, for example, can be understood quantitatively, so that decision strategies can strike the optimal balance between competing goals. Not using these methods is expensive: I estimate that insufficient use of mathematical methods in the development of pricing strategies causes European banks to miss out on annual profits between 500 million and 1 billion Euro from instalment loans alone.

4: AI requires effort

Machine learning and artificial intelligence are surrounded by an aura of effortlessness: Problems solve themselves. This assessment is inaccurate. 

Many underestimate that the introduction of AI requires good preparation and regular monitoring and readjustment. This starts with the selection of the appropriate method: not every algorithm is suitable for every problem. And a model is only as good as the underlying data. Accordingly, careful data preparation and cleaning is essential. 

5: AI must be explainable

Automated decisions using AI are highly scalable. This carries the risk that errors and undesirable bias replicate in an automated fashion. Decision logic that represents a black box therefore harbours incalculable risks. It is essential to understand how models work, and you must be able to see and explain which data characteristics dominate a decision — both on a global level and for an individual decision.  This is driving the increasing focus by businesses and regulators on explainable AI.

Artificial intelligence and digitalisation promise to significantly improve the cost structure in retail banking. As with all automation projects, however, it is important to use these tools carefully. But even intelligent technologies cannot magically fix mediocre processes. Therefore, it is worth focusing on the decisions and processes that deliver the greatest value to your organisation. When you do this, AI moves beyond being just another buzzword, and opens up new potential in retail banking.