Digital transformation in the essence of credit risk management
Digital innovation applied to credit portfolio management is the motto of this opinion piece, which highlights the importance of financial institutions being equipped with the latest techniques and tools. The focus is on Artificial Intelligence and the use of Machine Learning models.
Digital innovation, already featured on the agenda of most companies, is now a key issue for organisations. Its importance is evidenced both by the impact on process optimisation and by the reduction in operational risk or even by the potential associated with the return rate on investments in this type of innovation.
In 1965, American entrepreneur Gordon Earle Moore, later co-founder of Intel, predicted that the number of transistors in a processor would double every 18 months, while production costs would remain stable. A forecast that would effectively be confirmed during the following decades, becoming a reference for the entire computer industry. We see represented in this forecast the transformation and evolution capacities of technology up to the present day, with transformation becoming a key element both in society and in business processes.
It is this very transformation that can also help the financial sector and influence the maximisation of value for both the end customer and the financial institution itself. However, this will only be possible if the intended transformation is at the service of the institution's strategy and on the path of what is designed to be a real competitive advantage.
Speaking of sustainable competitive advantage implies that it is directly related to the efficient measurement of the risks arising from banking activity. This assessment can - and should - use predictive models based on algorithms that favour and support decision making. In the specific case of credit risk management, Artificial Intelligence plays a key role in the precise extent to which it is defined as "human intelligence performed by machine".
Thus, given the progress and expansion of economies, the increase in digital channels and the streamlining of business processes, particularly loan approval processes, there is a need for financial institutions to be equipped with the latest techniques and tools to monitor and protect credit risk management. A goal that can only be achieved through insightful and efficient analysis models.
The insight and efficiency present in the use of Machine Learning algorithms are also represented in Scoring models, where, from traditional models, pre-set criteria and weightings were used. Now, with the use of Machine Learning models, all data are scanned and analysed simultaneously, identifying and recording patterns without the need to follow a strict scheme.
This flexibility in Machine Learning models can assist financial institutions in identifying likely or unlikely combinations of customer conditions and credit offers that represent, respectively, a lower or higher probability of default.
The relationship between the historical analysis, the internal risk policies of each institution and the regulatory rules and guidelines make these predictive models solid and ensure compliance with the financial institution's business strategy. Thus, the access to data representative of the client's characteristics and the conditions of the proposal is of crucial importance. Therefore, the success of this implementation may also depend on successful access to and quality of historical data.
This methodology, based on behavioural analysis, enables the classification of a financing in a manner that is both insightful (through the rapid interpretation of a client's probability of default) and efficient (with a low probability of error), by formulating patterns and trends that support safe decision making and properly adjusted to the context in question.
In conclusion, these are the main advantages of the prediction applied to credit risk management, with an impact on the reduction of the NPL (Non-Performing Loans) portfolio and, consequently, on the reduction of credit risk.
We believe that these may be the solid foundations for an increase in the revenue associated with the credit portfolio, fostering an exponential growth of a financial institution's competitive advantage.
