Decision support system for the Bank based on Big data
Institutions in the Banking and Financial domain obtain a huge amount of data. Unfortunately, it is raw and unstructured that does not provide business growth and potential benefits realization.
Our customer – a European Bank and a leading player in the banking market since 1995 (further referred to as the Bank) – has encountered a similar issue.
For the last 20 years, the Bank has collected a large volume of data about its customers. But all the data was unstructured and fragmentary. A smart tool with Big Data analysis support was needed to process the information.
The Bank management wanted to increase sales and consequently revenue. For this purpose, it was necessary to:
- implement an instrument for data collection,
- design and deploy a tool for Big Data analysis,
- develop a software solution for data interpretation,
- build and deploy a program for finding dependencies in collected data,
- develop and implement a tool for data visualization,
- create an integrated solution for decision making using the results of data analysis and processing.
We had to perform a not trivial mission. RNDpoint’s team had to become a mediator between parties to ensure effective Data Mining and Analysis.
To increase the accuracy of results, we used the database of another partner of ours – Telecom operator. After a series of meetings, the agreement between the Bank and Telecom operator was achieved. They agreed that analytical data, received during the project, will be shared between parties and further used by both of them.
As known, Telco – the Telecom operator – has got a vast user database, which consists of the following data:
- age and sex;
- family status;
- purchase history, etc.
Our solution combines a database processing module and a custom software platform. The software platform includes the following subsystems:
- Business Intelligence (BI);
- Corporate performance management (BPM);
- Business Analytics (BA).
Business Intelligence gives the capability to collect, integrate, and analyze information at the enterprise level. The Bank’s employees have an option to explore, accumulate, and interact with the information. The Business Intelligence platform allows compiling on-demand reports and visualizing data to increase workflow efficiency and speed.
Business Analytics is used to exploit the most sophisticated statistical techniques. Innovative and smart data analysis approaches make a significant impact on data processing quality and predictive insights.
Business Planning Module is used to simulate, plan, and monitor the impact of decisions made by the management on the overall organization performance. BPM effectively links the core decision-making process at strategic key points with financial controlling, and profitability analysis with sales and operational planning.
RNDpoint’s developers, business analysts, and scientists worked hard to interconnect and make these all work together in a single integrated environment to support better decision making. As a result, the Bank got the unique software solution designed from scratch and smoothly integrated into existing banking infrastructure.
DB: Neo4j, MongoDB, Cassandra, Hbase, Redis
Hadoop: MapReduce, Yarn, Hive, Impala, Spark, Flink
Search engine: ElasticSearch (ELK), Solr
Adobe tools: Adobe Experience Manager, Sightly, etc.
Java: Spark, Drools, Jersey, Swagger, Jenkins
Oracle ATG Web Commerce
After thorough testing, the Platform enabled the Bank to make better, faster, and more accurate decisions with scalability and development potential.
The Platform implementation provided the Bank with advantages, such as:
- pin-point 97.8% accuracy in decision making;
- decision-making process became 3 times faster;
- data storage was increased due to cloud-based repositories;
- streamlining of workflow processes increased employees effectiveness by 32%;
- the Bank acquired a reliable partner – Telco – for further profitable user data exchange.