Preparation for better decisions

It’s not about AI making decisions on its own; rather, AI systems ought to create a solid foundation for human decision-making.

The zeb approach for optimized management support

The challenge

Providing decision-makers with precisely the information they need

Digitization and technologies such as big data, data analytics and AI allow users to leverage available data. Moreover, they constantly generate new data themselves. This should be taken into account by the decision-makers who are in charge of comparing alternatives or evaluating opportunities and risks. But there is not just more data; it has also become more complex in its structure. Relevant information must be compiled from different sources, different formats and different degrees of structuredness, into a decision-making model to be analyzed and delivered at the right time. 
Conventional methods of data analysis are not able to fulfill requirements of that complexity. Also, they have so far failed to deliver insights that give banks and financial services providers a long-term competitive advantage. What is needed are innovative data-driven methods to derive insights from large, complex, and heterogeneous data sets and support the decision-making process. These insights offer a clear competitive advantage: transparency and availability in real time.
Especially in complex and non-transparent situations, decision-makers need a tool that reliably assists them in finding, evaluating and preparing the relevant information at the push of a button, and in compiling the results into clear overviews. The aim is to provide options for action and to highlight risks by predicting future events using historical as well as current data. This makes financial services providers doubly future-proof.

Business data – a treasure hidden in your own cellar

AI digs faster, deeper and more precisely for added value

In addition to process data, which can be used to optimize workflows by means of process mining, business data is of particular importance in organizations. Business data is generated in the course of a financial services provider’s own operational business (such as customer, account and transaction data) or from external sources (such as market data or social media). Analytical methods for evaluating this data have been in use for many years. Modern methods from the data analytics and AI environment, however, open up innovative and intelligent approaches for gaining additional valuable insights. 
For example, AI methods can be used to recognize patterns in the data, i.e. regularities, repetitions, similarities, or implicit rules. This allows data objects to be independently assigned to groups (clustering), divided into given classes (classification) or put in context to one another (regression). AI methods can also be used to detect anomalies – or irregularities – that do not follow the pattern of the bulk of the data.
A typical use case of AI in finance is fraud detection. Traditional methods are based on a set of human-made rules that result in a binary classification (“fraud” vs. “no fraud”). However, using these models can generate a high number of false alarms. This may result in a loss of trust in these models so that actual fraud cases remain undetected.
AI-based clustering and classification methods can help reduce this issue of false positives. With these methods, the decision-making algorithms are dynamically adapted to the available data. As large amounts of data are continuously being generated, the model can take a more sophisticated approach in terms of discriminating between normal and anomalous behavior.

Our solution approach

Data-driven decision-making is part of our DNA. zeb’s roots are in advising financial institutions on various aspects of bank management. Data-driven decision models have been a part of this from the very beginning. With many years of professional experience in finance, we can offer you custom solutions tailored to your needs. We are happy to advise you on data-driven decision models: from the identification and evaluation of use cases to their implementation.

Feel free to contact us

AI is not a question; it is the answer to profound changes in the market for financial services providers. Do you want to make decisions based on a stable data foundation? We look forward to helping you tackle your challenges!