Information Technology (IT) is no longer only the steward of middle- and back-office functions, but a critical business partner to growth. In this three-part whitepaper series, we’ll explore the key challenges that financial institutions’ IT functions must address: legacy modernization, in which we’ll discuss how to bring legacy systems into the 21st century; data management, where we’ll reflect on the need to access and aggregate data for regulatory reporting and customer facing initiatives; and digital transformation, where we’ll take a look at how to manage the mobile, omni-channel, and API technologies that bring value to customers. At the core of each of these challenges is the need for a new level of connectivity.
To thrive in this era, financial services organizations must seamlessly integrate applications, data and devices and it is those organizations that are able to embrace these challenges – those “connected financial institutions” – that will win.In the first installment in our Connected Financial Institution whitepaper series, we discussed how aging back office systems, operational effectiveness and open source adoption are driving legacy modernization initiatives across the financial services industry. But the story doesn’t stop there. Modernizing legacy systems is only the first step in addressing the business imperatives faced by financial institutions.
Accessing, aggregating and transforming data is critical whether for regulatory compliance reporting, understanding the customer, or being able to manage large data sets for predictive analytics. Financial institutions face significant challenges when unlocking, aggregating and managing data across the enterprise to achieve these strategic business objectives. This data is often siloed in legacy back office systems and line of business-centric databases, using disparate data definitions and technologies.
This presents a daunting challenge to IT professionals tasked with creating and managing development environments that can meet demands from internal and external stakeholders to quickly deliver information from across the enterprise. As Dana Edwards, CTO at MUFG Union Bank, N.A, said: “The traditional approach to IT and development is no longer adequate for today’s business environment.”
Data Management Challenges in Financial Services
There are three major categories of data generated or used in financial services:
- Customer/client/policyholder data may include contact fields, products used, transactional information, customer service inquiries and demographics
- Credit/financing/policy underwriting data may include financial, purpose, health, actuarial, collateral and appraisal
- Risk/compliance/fraud data may include loss, watch list, suspicious activity, statistical parameters and audit results
These data elements sit in various, purpose-built data stores across an organization, with unique data definition, data model, information security, and user access requirements. But in order to address the business drivers of regulatory compliance, customer centricity and data-driven decision making, information must be able to flow through the enterprise while respecting data governance dictates.
Business Imperatives for Transforming
Data Management Financial services firms are facing a rapidly changing and evolving set of business imperatives such as an increasing regulatory and risk management burden, deepening customer relationships to improve customer retention, and leveraging increasing amounts of information to enable strategic decision making. Financial institutions need to transform their data management approach to unlock data from legacy application silos, incorporate unstructured information from non-traditional sources, and integrate applications on-premises, in the cloud as well in hybrid integration scenarios.
Improving Data Driven Decision Making
Analytics and business intelligence have always been critical in financial services. Consider the analytics performed by actuaries to assess the likely outcome of various insurance scenarios. Or the credit risk scoring analytics involved in judging a borrower’s likelihood of repaying a loan. And the analytics used by investment bankers to evaluate merger and acquisition transactions. What’s changed is the era of “big data.”
As the Harvard Business Review puts it: “Whether you work in financial services, consumer goods, travel and transportation, or industrial products, analytics are becoming a competitive necessity for your organization.” ⁹ Financial institutions are facing a flood of information, but its not enough to create a massive data warehouse with every piece of information imaginable. The promise is in “data driven decision making”, defined as “the practice of basing decision on the analysis of data rather than purely on intuition.”
Best Practices for Data Management
“Big Data” itself was historically its own data silo with rigid extract, transform and load (ETL) processes moving data from on-premises source systems to a tightly-controlled data warehouse. When lines of business wanted data analytics, they sent a request to the data warehouse quant jocks and hoped that the query returned the data they were looking for. This approach no longer works for financial institutions looking to streamline regulatory reporting, monetize customer analytics, and democratize data-driven decision-making. These business drivers, along with innovative, cost effective technologies such as Apache Hadoop and NoSQL databases, are spurring financial services firms to invest in next-generation data management.
To successfully enable the vision of becoming a “Connected Financial Institution” the integration architecture should have three distinct layers:
- Data Access Layer: Utility services that facilitate data retrieval from key systems of record
- Orchestration Layer: Processing logic that transforms and enriches data Data
- Presentation Layer: APIs or services through which consumers are provided governed and secured access to data