How Graph Technology & AI Helped a Global 50 Bank Lend in Latin America
The ChallengeA two-year stint of negative growth in the LatAm region happened due to a number of factors: changes in Chinese economic activity, a strong U.S. dollar and a sharp decline in commodity prices. These were compounded with domestic political instability, macroeconomic fragilities and corruption scandals.
On top of negative growth, the private sector was experiencing lower credit provisions. Factors included differences in GDP per capita, weaker legal rules to enforce creditor rights and high information asymmetry due to inadequate debtor data.
Marionete was already working with the Global 50 LatAm bank to help minimize exposure to credit risk. However, the towering quantity and expanding diversity of data spanning the bank’s siloed databases presented an extreme challenge.
“Beyond effectively managing the data,” said Ricardo Miranda, Big Data Engineer at Marionete, “we couldn’t generate the real-time insights necessary to quickly identify and respond to threats of credit risk from borrowers.”
“If a bank is unable to interpret data quickly to discover the relationship between customer credit risk, as well as if debt interest is paid on time (if at all), you’re incapable of evaluating and understanding the exposure level of each and every one of its borrowers,” he added.
The bank’s legacy technology simply couldn’t manage such sweeping amounts of data.
“With various data sources in use, many based on traditional relational database technology (RDBMS), the volume and diversity of data the bank generated couldn’t be harnessed to render real-time analytics and recommendations,” Miranda said.
The SolutionMarionete addressed the bank’s data challenge by integrating its various databases, including RDBMS, using the Neo4j graph database. With this level of insight, the bank was equipped to mitigate credit risk and influence bank charges and interest rates.
“With Neo4j graph technology,” said Miranda, “we were able to gain a deeper understanding of the bank’s borrowers, such as their relationships with other economic agents like suppliers, financial intermediaries and customers.”
The digital transformation project was twofold. Marionete created the master data “blueprint” that integrated all of the bank’s disparate data. Marionete also implemented an extensive inhouse training program across several of the bank’s divisions.
“With Neo4j installed, we were able to create a data model that relays borrower’s credit scores,” said Miranda. “At the same time, the graph database solution provided the bank with a complete view of master data, all made available in real time.”
Now, Neo4j has become the cornerstone of powerful insight into the bank’s data, helping to reduce credit risk, empower decision making and identify new business opportunities.
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