Financial Institutions and their Data Integration Challenges

Data in rogue silos is the primary reason which prevents financial enterprises gaining a competitive advantage. IT complexity and silos make it difficult for financial enterprises in leveraging information for strategic decision making and improving customer experience.

Financial institutions mainly use three types of data:

  •         Customer Data: This form of data includes contact fields, products delivered, transactional information, customer inquiries, demographics, etc.
  •        Credit Data: This includes information like financial, health, actuarial, and appraisal data
  •         Compliance Data: This data includes loss, watch list, suspicious activity, statistical parameters, audit results, etc.

Financial systems generate these data types in various formats and models. Start-to-End data integration holds the key to improving regulatory compliance and customer centricity, etc.  Integration helps in re-architecting connectivity, eliminating silos, and moving data between source and target systems in a multi-dimensional environment.

This data comes from wide variety of sources in wide formats. Conventionally, financial enterprises used developer tools and hand coded approaches to combine this data in a common format. A lot of expertise and coding is required to operationalize these developer tools. The middleware is too complex and cumbersome to use. Using these tools for processing multi-GB data between source and target systems becomes a painful undertaking.

Accelerators for financial data integration help in getting a 360-degree view of customer, delivering services in a better way and improving customer experience. These accelerators help in setting up a future ready infrastructure that responds faster to business and technology changes.  

Financial enterprises are facing a growth in demand. They need to modernize their IT for dealing with a rapidly changing business imperatives, and improving compliance & customer experience. To do this smoothly, financial institutions need to embrace the right data integration approach. The right approach lets them unlock data from silos, combine heterogeneous and hybrid IT applications, and integrate structured and unstructured data.

Another relevant challenge is large file data processing. Processing data in multi-GB data can be an uphill challenge for enterprises. Enterprises use batch processing for processing data. They parse data in small chunks and aggregate these chunks post processing. This method is error-prone and cumbersome. Some enterprises use costly appliances and Big Data tools for large file data ingestion and processing. However, these appliances need heavy set up, developer support, and databases. Financial institutions taking a holistic view toward data management across regulatory compliance, customer centricity, and big data projects have an opportunity to optimize the use of data across the organization.

Real-time integration can help financial enterprises in beating back these challenges. Advanced financial data integration solutions help enterprises in setting a framework that

  •         Data Access Layer: for retrieval of data from key system records
  •         Orchestration Layer: For transforming and enriching data
  •         Data Presentation Layer: For governing and presenting data.

Business users get out-of-the-box connectivity to integrate financial systems and allow rapid buildup of data layer. Custom message processing components enable users to process data without problems. The same platform can be used to expose data without problem. The ability to connect and orchestrate applications provides 3x to 6x agility to enterprises, improves services delivery, and accelerates time to revenue.

Adam Hansen

Adam is a part time journalist, entrepreneur, investor and father.