Technical game changers in the financial industry

Robert Meters, Editorial Board Member of TRF News and Director of SCHUMANN International Limited, describes the advantages and challenges of using AI/ML technology to obtain digital transformation of the processes.

It has become very clear recently that technology is essential for business to explore business opportunities. Corporates, banks, financial institutions and credit insurers schedule investments in digital transformation processes. During the economic crisis caused by the COVID 19, a sustainable awareness of IT-supported processes has emerged, which will continue to be important even more important in the future. Furthermore, advantages of digital decision-making processes have become obvious and reservations about automated processes have been reduced.

Banks and financial institutions active in receivables finance estimate that the use of API (application programming interface) technology for accessing relevant data and AI (artificial intelligence) and ML (machine learning) are among the key impacts in the transformation of processes but are difficult to implement on their own. Regulators also demand traceability and transparency in the application of these technologies. In addition, data protection must be taken into account when collecting and using personal data from the context of the IoT (internet of things), social media and from partner networks.

Other challenges for the financial industry include the requirements of the EU's Green Deal, which is setting the pace for a development towards sustainable financing models. Interacting with this, investors, banks and credit insurance companies are moving their assets into sustainable financing models and asking industry and trade to incorporate ESG goals into their corporate strategies.

On this basis, automatic, AI/ML-supported data analyses and decision engines can be used in the finance industry for specific tasks e.g., fraud detection and breaches of ESG requirements as well as payment and credit risk analysis. An important field of application of AI/ML is the pattern recognition of possible fraud cases and the derivation of alerts. The objects of these methods include master data analyses, invoice analyses e.g., detection of fake invoices without real services, as well as conspicuous bank details. In the future, social media data can provide additional relevant information that is not available in classic data sources. Furthermore, credit risk forecasting methods are applied and tested. A concrete area of application could be the derivation of the payment probability and liquidity situation for individual transactions regarding complete portfolios and thus the adjustment of risk mitigation strategies.

Smart technologies have prepared the ground for IT solutions for SMEs and their technological impact on business will increase. Non-bank financial institutions (NBFIs) and fintech platforms are expected to play an increasingly important role e.g., in cross-border receivables financing for SMEs. Traditional banks and financial institutions have already applied cutting-edge methods and technologies in business transformation projects to achieve seamless integration of new technological components, automation and the ability to quickly adapt decision-making engines and processes to changing market and risk realities. Financial institutions and banks need to achieve the ability to scale the business through technology by increasing efficiency, reducing processing time and thus lowering costs, improving the quality of processes and decisions, and ultimately increasing customer satisfaction.

Technologies such as AI/ML as well as the expansion of the use of digital data source in fast digital networks can become important game changers in perspective. However, in order to unlock its full potential and overcome today's challenges, the whole pipeline consisting of data, modelling and evaluation has to keep up with future visions. More valuable data and data analysis results will flow directly into digital end-to-end processes. Essential for competitively differentiated AI/ML supported decisions is the alignment of the systems with the user's concrete business model.

It is obvious that further developments in a society and an economy are largely determined by technological progress. The trend of digitalisation and corresponding product innovation will very much prevail. In contrast, the issues of human vs. machine will become even more prevalent in the consciousness of society and lead to a critical discourse, as humans believe they must retain a certain degree of manual influence.

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