Best Practice

Credit Risk


Amongst the business sectors that have contributed the most to the “digital transformation”, Financial Services is at the forefront. Since the beginning of the 2000s, technology has evolved very quickly from the first home banking systems to modern credit institutions that operate online or exclusively online. Digitalization is more than a choice but a mandatory path to meet the needs of optimization, reduced response time and above all “24x7-availability”.

Amongst the greatest consequences digitisation can face, is the evolution of loans processes in a digital and paperless perspective. With the use of advanced decision-making engines, complex business rules and customer evaluation models can be implemented to allow faster and more efficient processes.

Implementing a workflow for loan granting and monitoring, using advanced BPM methodologies and advanced document management tools, allows banks to reduce time-to-market, improve customer experience and increase the level of control over the end-to-end credit management process. In this context, by using advanced risk assessment models, banks are able to analyze transactions in real time and reconstruct the customer's financial dynamics, using behavioral data, social reputation and other "digital" information of its customers, overall reducing the time required to approve the loan request.

Lastly, the digitalization of processes helps banks to increase data quality and to respect regulatory obligations by acquiring and storing data via a directly at the source.


The drastic increase of Non Performing Loans in Europe over the last ten years, coupled with new and invasive regulatory reporting requests, are forcing banks and financial institutions to think more broadly about how they manage NPLs and credit risk in general.

New regulation and supervisory reports require detailed information on non-performing exposures and increasingly, require specific information about collateral that can mitigate both the status of the debt and favor the recovery procedures.

Today’s banks must have the adequate know-how, experience and expertise to effectively manage NPLs, increase recovery rates, and simultaneously absorb the constantly evolving regulatory framework. Along these lines, banks should consider the prevalent strategy of creating dedicated business units within the bank, with specific yet expanded responsibility for NPL management.


Today’s data driven banking creates a major need for banks to get a handle on their capacity to correctly and efficiently model data. Whether it be rating models or surveillance models, banks are looking for ways to gain both a competitive edge and results. Among the most promising options for modeling are Advanced Analytics and the application of Machine Learning.

With Advance Analytics, banks can expect to gain insights into data and learn from results thanks to sophisticated tools and techniques.

By relying on Machine Learning to enhance current methods, modeling has taken the "natural" next step of becoming reactive to historical data to tell us what will happen next.

Early warning and other predictive models can be positively impacted as well. Thanks to advancements in how enormous amounts of data and variables are managed, banks can now let machines survey data and teach us how phenomena are correlated.

Of course to leverage Advanced Analytics or Machine Learning and get the expected return, banks must in any case remember to place a large emphasis on data quality, the maintenance models, and the maintenance of IT systems in general.  


- Credit process management advisory and consulting services, to design and implement end-to-end process for credit granting and monitoring

- Consulting services on Regulatory reporting on credit, to identify requirements for a correct and efficient reporting process

- Process governance documentation services, to deliver policies, procedures, report templates, and controls;

- Credit and operational strategies implementation;

- Definition and design of advanced risk assessment models;

- Definition of data model and interface adaptation;

- Consulting services for NPL regulatory assessment;

- NPL cost and forward-looking models implementation;

- Advance modeling consultant teams;

- Advanced analytics and machine learning best practices.

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