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O futuro das finanças na América Latina e no Caribe



Credicorp modernizes data management as hyper-personalization beckons

Mar 14, 2022

By Antony Pinedo
Credicorp capital

Data cleansing is essential for banks to be able to apply sophisticated analytical tools, and Credicorp Capital is tackling the issue head on

Credicorp Capital, the investment banking arm of Peru’s Credicorp group, is getting to grips with a problem facing many banks in Latin America—poor quality  data.

The bank has introduced a new data management policy in a bid to improve the analysis of information about customers and the bank itself. The move is linked to the growing trend of hyper-personalization financial services in the region. Cleansing the data is a crucial step. It can reveal the data’s true potential for increasing revenue by allowing banks to offer predictive, intelligent services to digital users.

Some 78% of banks globally employ data in their operations, but only 7% have scaled it with analytics and just 5% using artificial intelligence, according to an Accenture report published in mid-2021. The figures show on the one hand banks’ latent interest in capitalizing on better services, and on the other, that the utilization of financial data—despite its abundance—is far from reaching its potential.

“What's the basis of data and analytics? It's having good data," Juan Carlos Oré, Credicorp Capital’s chief data officer, tells iupana from Lima. "It sounds simple enough, but it's a super titanic task and data management isn’t always given the importance it deserves."

"Identifying the right source and that it’s quality data is the basis of data governance. That's the first thing to be done," he adds.

And while analytical tools such as artificial intelligence and machine learning "are very sexy", implementing them before organizing data silos is a strategic mistake, says Oré.

The investment bank, which is part of Peru's largest financial services group, began its data management project by identifying duplicate sources of information and then implementing a unique vocabulary within the company, which helped it reduce the time needed to prepare manual regional reports from ten days to just two.

And in the next three months, it expects to complete a project in Peru, Colombia and Chile that will allow it to measure the composition and profitability of investment fund portfolios quickly and efficiently.

“It’s important that what you focus on is aligned with what the business needs," says Oré.

Read next: Empathetic Banking, or how to put customers at the heart of digital finance

Opportunities for proper data management

Banks that succeed in mastering data-driven analysis to get to know their clients better can increase the return on their investments tenfold, Nicolás Deino, executive director of Accenture in Chile, tells iupana.

“If traditional banks rethink their business models and adopt the innovative strategies [employed by] the new operators in banking and purely digital financial services, they could increase their revenues by almost 4% per year, which would mean more than half a trillion dollars of additional income by 2025," says Deino.

"The great opportunity for banking today is not only to focus on collecting data but to analyze and segment it through new technologies to make better decisions."

Those new technologies include machine learning and artificial intelligence—tools that are beginning to show their potential for separating pears from statistical apples. It’s also about the storage of information in the cloud, where records can be processed more easily than when they are stored within on-premise systems. In addition, it’s a cheaper alternative to physical data warehouses.

Credicorp Capital is the first company within the group to create a data lake in the cloud.

“A bit of data governance is being implemented as part of every project that ultimately brings value to the business. And little by little, like building Lego, it will eventually be possible to implement this program throughout the company, "says Oré.

Information processing is also beginning to alleviate bottlenecks in day-to-day operations. For example, the bank’s expense and accounting departments used different computer programs that didn’t talk to each other, which prevented them centralizing numbers and made the process of closing each month a “terrible” task, he said.

Read next: Banco Inter: Brazilian digital bank prepares for U.S. listing, international growth

Data cleansing in banking

The urgent need to prioritize data and clean it up is one of the most complex challenges facing the financial system in Latin America. Outdated, erroneous or duplicate information is referred to as dirty data. In practical terms it’s useless to the bank and a huge investment may be required to cleanse it.

Between 2018 and 2019, losses produced by dirty data reached US$13 million in Mexico, with the most affected industries being banking and telecommunications, according to statistics collected by Finerio Connect, a provider of open banking solutions.

"The data is now quite dirty because it goes through acquirers, through payment methods, through different companies [...] LatAm has a pretty big dirty financial data problem that doesn't allow it to automate," says Nick Grassi, Co-CEO and co-founder of Finerio Connect.

What’s more, most traditional institutions in the region don't know for sure where their data is or how to purify it, Grassi adds.

This diffuse data costs analysts’ in terms of efficiency as they spend most of their time organizing and filtering information manually.

"That's what happens in many companies. Analysts don’t analyze because 80% of their time is spent looking for the information, trying to get the data, put it together, compare it and in the end, they have very little time left," says Oré, of Credicorp Capital.

At an event hosted by iupana and Backbase, a financial software developer, industry leaders agreed that too much data can choke banks and complicate development of customized products, which is something customers are demanding.

The key seems to lie not in volume but in prioritization.

"The problem is that the data has not been used to the fullest," says Grassi.

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