Monetising AnaCredit reporting data

Monetising AnaCredit reporting data

Throughout history, economic crises demonstrated that different economic sectors react in different ways to economic shocks. It became clear that basic data aggregation would not be sufficient to fully understand the dynamics and developments in different sectors and geographies. In order to maintain control and ensure the health of the financial system, the European System of Central Banks took action.

In 2011 the ESCB initiated the Analytical Credit Dataset Project, which aimed to create a database of information regarding the loans granted by banks in the euro-area. With the ECB’s AnaCredit Regulation of 2016, National Central Banks are required to collect granular data from resident credit institutions, who must report 95 attributes at the individual loan level.

All of this loan-level data sits in a European database on credit and credit risk,  managed by the ECB and supporting several central banking functions such as decision-making in monetary policies and macroprudential supervision.

Of course, this level of reporting comes at a cost. AnaCredit is expensive. Some wonder if such a project might overburden small institutions. In response, the ECB implemented a “Merits and costs” procedure that would turn out to be cost-effective and even exempt a large number of banks from the reporting.

But is it enough to just lower the costs for certain banks? Should banks be able to leverage this data to aid in their decision making when it comes to credit decisions and, in this way, gain a true return on investment (ROI)?

Well, there’s good news and bad news. 

Starting with the bad news – regular banks cannot access the combined dataset to leverage this rich data. 

The goods news – your bank collects this data to send it to AnaCredit and it is therefore already available by definition.

Of course, banks aren’t accustomed to using this volume of data to make credit decisions… yet. The transformation of financial services is underway and many credit institutions are realising the benefits of sophisticated bigdata methods such as machine learning and, more specifically, deep learning.

Deep learning algorithms have been shunned by financial services due to the lack of explainability, but that is quickly changing. Future-thinkers are creating inherently explainable models to ensure that your credit decisions are based on truths and not possible falsehoods. In fact, Algoritmica is also on the verge of cracking explainability

With that hurdle out of the way, your credit institution will be able to handle large amounts of data to extract relevant information that will bring several benefits. In fact, advanced predictive analytics are somewhat necessary in this time of uncertainty, as payment histories are not enough to determine a borrower’s ability to repay their loan.

By leveraging the 95 attributes of the AnaCredit reporting data, you will gain true insight into your customer’s life and behaviour. These attributes can be used as raw data to train risk models, providing a plethora of rich information to improve risk calculations.

As a result, you will be able to predict your clients’ behaviour before they even know what they will do. The ability to do this many months in advance will introduce you to a delicious opportunity: the chance to reach out to negotiate new terms before the loan goes into default.

This will save you from dangerous losses and, consequently, give you the power to increase your lending volumes. 

In conclusion, originators should leverage the data lakes and infrastructures they built to comply with, but not only, the Anacredit Regulation. This can be done both internally and externally by adopting new-age solutions in order to finally monetise the data instead of “only” complying with regulatory requirements. 

Doing so will offer a true return on investment and a way to improve the credit decision-making process.