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How MuleSoft Fuels Data-Driven Decision Making for Payments

May 3, 2021


Data Integration and Information Management

Data-driven decision making is critical for payments companies, yet challenges from legacy systems and applications and data management remain. MuleSoft’s Anypoint Platform helps organizations shorten deployment cycles, save costs, and improve ROI.

Data is at the heart of everything in payments. Data-driven decision making (DDDM) is the force behind faster, streamlined, secure payments. Using metrics, data, and analysis to guide strategic business decisions enables the creation of innovative payments products and solutions. While data can empower everyone to make better decisions, there are some considerations that are table stakes to effective DDDM.

Thoughtfulness is critical when seeking helpful answers from data. Back office systems and operational effectiveness can impact DDDM for financial service providers, placing digital transformation and modernization front and center when it comes to all things data.

The ability to aggregate and transform data has become paramount, not just for regulatory purposes but also to make critical decisions around customers and to manage for predictive analytics. Many payments companies are battling with legacy back-office systems and disparate data. IT professionals that must manage development environments and deliver data enterprise-wide face increasing pressure even as both data and the organization become more complex.

Batch processing in a data-warehousing environment is quickly becoming obsolete as data management now requires more real-time integration across many systems and stores, including back office, SaaS applications, analytics, social networks, and more. Holistic integration across the enterprise paired with an agility layer is essential for a forward-looking data management architecture.

Challenges to Data-Driven Decision Making for Payments

Financial service providers, including payments companies, must be able to navigate through data across three main buckets:

  • Customer data: This may include contact information, transaction history, products or services used, and demographic information.
  • Credit or underwriting data: This may include a wide range of data that covers financial, collateral, appraisal, and actuarial data.
  • Compliance, risk, and fraud data: This may include suspicious activity, audits, watch lists, and parameters.

In many cases, this data lives across the enterprise, resting in purpose-built data stores that have proprietary data models, data definitions, and access requirements. This setup flies in the face of the purpose of each of these buckets of data, which requires the free flow of data throughout the organization in order to meet compliance needs, meet customer-centric objectives, and drive effective and accurate decision making.

Add to that the highly complex regulatory landscape with its myriad of laws, rules, and standards and it’s a recipe for a very convoluted data processing experience. In nearly all cases, connectivity must be re-architected so that necessary data elements can be pulled from disparate sources, standardized, and reconciled.

Integration Enables the Flow of Information for DDDM

Batch, point-to-point integration, and a lack of master data management (MDM) strategy feed data quality issues. These issues tend to snowball over time as changing internal and external endpoints create significant application development burdens that, without a reference architecture, lead to non-standardized solutions that only make integration more difficult over time.

The antidote is a holistic approach toward data management that covers compliance, customer data, and other big data initiatives and that streamlines the enterprise-wide use of data. Integration is at the heart of the issue and can facilitate this flow of data alongside an agility layer.

Unifying data sources and improving data collection from additional sources mean organizations face skill set and resource constraints in addition to infrastructure gaps. One possible solution is adapting the architecture and using integration tools like MuleSoft’s Anypoint Platform.

As a leading connectivity platform for SOA, SaaS, and APIs, MuleSoft’s Anypoint Platform can be a viable solution for payments companies to future-proof data management initiatives. Not only does it provide a unified integration architecture for loosely-coupled applications and data, but it can also wrap legacy system functionality, introducing reusable REST APIs, web services, and microservices. This means payment companies can avoid re-architecting the integration layer while accelerating data management initiatives.

With a library of over 120 connectors and best practices templates for both on-premise and SaaS, MuleSoft provides organizations with a framework for future integration needs — a critical component as payments organizations increasingly leverage SaaS packaged applications for administrative functions. MuleSoft helps integrate data from those systems with data from legacy systems and applications.

Even more critically, MuleSoft’s Anypoint Platform is easy to understand for developers, requiring minimal retraining, and can be rapidly deployed.

Integration is the backbone of data-driven decision-making for payments. Considering the breadth of integration, the push for legacy modernization, digital transformation and data management, and the total cost of ownership, MuleSoft’s Anypoint Platform presents a leading integration solution that can help payments organizations shorten deployment cycles, save costs, and improve ROI.

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