Customer
A large omnichannel bank with an extensive branch network. It is one of the 100 largest banks in the world in terms of capital. The bank's client base exceeds 13 million clients.
Challenge
Timely calculation of financial and risk metrics. Formation of credit risk monitoring signals.
Project Results
Data Lake was created to collect, process and calculate financial and risk metrics. The analysis of metrics was carried out. Signals for monitoring credit risk were generated. A sandbox has been deployed for the work of risk analysts.
Business Results
Significantly improved the speed of borrower assessment and the quality of credit risk assessment. Reducing the response time to credit risk events in relation to bank customers. An assessment of the outflow of customers and a forecast of account balances have been implemented.
Tech Stack
Hadoop (Cloudera), Apache Spark, Apache Impala, Spago BI, Jupyter Hub, H20 Sparkling, Anaconda.
A large omnichannel bank with an extensive branch network. It is one of the 100 largest banks in the world in terms of capital. The bank's client base exceeds 13 million clients.
Challenge
Timely calculation of financial and risk metrics. Formation of credit risk monitoring signals.
Project Results
Data Lake was created to collect, process and calculate financial and risk metrics. The analysis of metrics was carried out. Signals for monitoring credit risk were generated. A sandbox has been deployed for the work of risk analysts.
Business Results
Significantly improved the speed of borrower assessment and the quality of credit risk assessment. Reducing the response time to credit risk events in relation to bank customers. An assessment of the outflow of customers and a forecast of account balances have been implemented.
Tech Stack
Hadoop (Cloudera), Apache Spark, Apache Impala, Spago BI, Jupyter Hub, H20 Sparkling, Anaconda.