Banking Process Digitalization

Client: Global leader in Banking and Fintech

Technology

jBPM​,Java 1.8,​RxJava,​Spring Boot , Spring Data, ​MapStruct, ​REST APIs​

Services

Automation of banking global workflow, monitoring and reporting application to meet the requirements of Lending & Deposits and Credit Risk Management, customers, trading partners and legacy systems.

Challenges

In the way of leveraging trading partners as well as existing information systems and legacy applications for customers, credit ratings, credit risk management, loans, lending and deposits information the following limitations were encountered:
  • lack of risk data accuracy and integrity for credit decisions
  • audit requirements to reduce fragmentation
  • non-automated workflow support for Structured Credit Analysis
  • process breaks, manual workarounds/inefficiencies on processes
  • lack of transparency and traceability of processes and data for Lending & Deposits
  • lack of real-time monitoring
  • slow and inconsistent pricing exception approval tracking and execution

While analyzing common patterns for source of bottlenecks in banking and financial ecosystems, it turned out that one of the most expensive factors in terms of delays,resource allocation, adaptability to changes and customer needs would be considerably improved should a custom business process integration layer is added, in order to:
  • address the communication and process execution gap between business users and IT, with business process management capabilities,
  • allow orchestration of services and integration of business requirements
  • alleviate the barrier of communication amongst multiple channels and siloed departments,
  • minimize the costs, time and resources required to build and maintain them.

Time was of the essence and delivery was to be achieved within 4 months.

Solution

The current solution was chosen considering the business context of the customer, existing digital transformation maturity level of the organization, regional distribution, overall ecosystem and the degree of connectivity for existing processes. Logical data model has been divided into three data groups:
  • Borrower related data
  • Pledger related data
  • Facility / CAM related data

This data has been distributed into several entities.

Results

  • 6 digits figure yearly cost savings – from not renewing the TIBCO license
  • Increasing business process visibility at Organization level and facilitating the loose coupling of systems and actors.
  • Impact of automated workflows improved the audit regulations meet of criteria,
  • 15% better customer response times,
  • 35 % reduced effort spent on platform monitoring and cross-systems health checks and redundant work tasks, made more visible the business heatmap,
  • generated a unified DataModel linking corporate strategic objectives to KPIs and automatic reporting representations,
  • 20% faster release to the market for process extension and changes meeting the service quality and compliance standards.   

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