Case study: UniCredit uses Fico to apply prescriptive analytics to risk management

Data for decision-making

The adoption of Fico reflects UniCredit’s strategy on data and analytics, and it’s the foundation piece in creating a new way to enhance customer relationships and credit risk management. 

“Our goal is to get actionable insights resulting in smarter decisions and better business outcomes. How you architect business technologies and design data analytics processes to get valuable, actionable insights varies. Fico allows us to put in place a prescriptive analytic environment. Prescriptive analytics automatically synthesises big data, mathematics and business rules to suggest decision options to take advantage of the predictions,” says Cavinato.

“That helps UniCredit in its day-by-day operations. Further, prescriptive analytics can suggest decision options on how to take advantage of a future opportunity or mitigate a future risk and illustrate the implication of each decision option. In practice, prescriptive analytics can continually and automatically process new data to improve prediction accuracy and provide better decision options.

“I see Fico as a subset of the analytical environment, and I’m sure it’ll help us to further extend the intelligence we get from data,” he says. “Data culture is all about democratising business intelligence. It basically means that users – whether they are in operations, frontline or back-end support – should all be able to analyse the data that is flowing across the system.”

According to Cavinato, the adoption of Fico is tightly integrated to a new vision of the whole enterprise data infrastructure. 

Our goal is to get actionable insights resulting in smarter decisions and better business outcomes

Ivan Cavinato, UniCredit

“We aim to build a more flexible and agile architecture. That also means displacing pieces of legacy software and embracing distributed architecture, such as Hadoop. But let’s be clear. That doesn’t necessarily means dealing with unstructured data,” he says.

“New distributed frameworks, such Hadoop, create efficiency at the processing level, introducing new efficiency at the operational level and consequently reducing time to action. Last but not least, re-engineering the data infrastructure according the new Hadoop and big data paradigms can lower the overall cost. The previous software didn’t allow us to evolve in that direction.”

Building a seamless business structure

The logic behind Fico is also of the utmost importance for UniCredit because it can be adapted to other lines of business. 

“We want to break down the barriers and silos that continue to exist within a big organisation,” says Cavinato. “Data has to be seamlessly available across different areas. The way we’re going to work it’s crucial to create a more integrated business between us and the marketing side. As soon as we brought Fico inside the organisation, the marketing team became very interested to understand the intelligence that it provides to further enhance the CRM [customer relationship management] operations.”

Cavinato sums up: “Fico underpins a software methodology that’s largely dependent on algorithms. It’s a key building block to proceed to a complete overhaul of the entire infrastructure, physical and logical, that supports our data business. It helps redefine processes with greater agility and granularity, bringing new opportunities and greater performance. 

“If you like, it is the same story that has been experienced at the factory level with the adoption of the concepts that led to lean production in the manufacturing world. Our efforts go in the same direction – the difference is that we want to apply those concepts to the business processes. The risk management project we’re using Fico for is a turning point that will help UniCredit to leverage the new emerging opportunities.”

UniCredit Bank image courtesy of Pavel Ševela/Wikimedia Commons.