Helping Banks Meet Regulatory Compliance with Big Data

"Big ‍10 ‍banks fined $43bn over seven years for failures in customer reporting” reads yesterday’s headline in Financial Times and I wonder how the power of big data could have helped in saving these billions of dollars. The report goes further: "Failures in customer reporting have cost the world’s top investment ‍banks $43bn in fines over the past seven years, making it the single most expensive compliance issue”.  

In the age of advanced reporting capabilities enabled by big data, this seems shockingly backwards. This is especially true when you consider how many industries are increasing profits by using customer data to upsell-cross sell and targeted marketing. By using robust technologies like the MapR Converged Data Platform to build a 360 degree view of the customer, I reckon that banks could have saved billions of dollars on regulatory fines in addition to generating profits and cutting costs.  

Compared with other industries like telco and retail, financial services are playing a bit of catch-up on big data and digitalization wave in APAC. While they may some years behind other industries, interest in the possibilities of big data have cranked up enormously across financial services in APAC region now. Much of the hesitation to adopt this new technology has been attributed to conservative and cautious nature of banking industry. 

But revenue constraining fines and stringent regulatory frameworks have increased pressure on banks to meet regulatory requirements as quickly and as promptly as possible. The power of big data - correctly applied - can help banks reduce regulatory compliance risks, and avoid potential problems in real time. Big data for the banking industry the world over is becoming more of a necessity than a matter of choice.

Regulatory Compliance Reports

Big data analytics can help build new compliance reports and perform regulatory stress tests. The annual stress tests by regulators require banks to aggregate data that is scattered across systems, servers, apps, RDBMSs, lines of business, and separate legal entities. Hence, updating data and sourcing the adequate data are crucial to the stress-testing process. The MapR Converged Data Platform plays a key role in empowering the banks to easily ingest data from both new and legacy sources. As banks maximize the value of their data by feeding it into a variety of application and analytical models, they should use both internal and external data source to consider these models from regulator’s perspective. The client fines that are hitting these banks are also due to investor protection, transparency and transaction accuracy as well. Including misleading communication or fraudulent agent activity. 

MapR Converged Data Platform can again help to not only report aggregate client data, but also use a wider breadth of data inputs (email, voice) to understand what is underpinning the credible differences seen by auditors. All of this is done in a more timely and consistent manner with Converged Data Platform. These regulations are going to continue with more stringent increase in scrutiny and on a more global basis. 

Reducing the High Cost of Regulatory Compliance

The high cost of meeting regulatory requirement is also burdening many banks. However, violations of these requirements are expensive and sometimes unaffordable for smaller banks that generate lesser capital than bigger banks. Big Data analytics driven by a robust data platform will prove more fiscally beneficial in the long run in comparison to the potentially billions of dollars in fees and fines over small errors or oversights.

Solution: MapR Converged Data Platform

MapR provides the industry’s only converged data platform that integrates the power of Hadoop and Spark with global event streaming, real-time database capabilities and enterprise storage. This enables financial service organizations to harness the enormous power of their data and meet ever increasing regulatory compliance needs.

The Apache Hadoop distribution from MapR provides full data protection, no single point of failure, improved performance and dramatic ease of use advantages for financial sector technologists. MapR has also developed Quick Start Solutions which can helps banks develop key use cases to meet regulatory requirements in 4-6 week time frame. MapR Quick Start Solutions are a set of purpose-built solutions for the most critical and valuable use cases for big data and Hadoop.

The MapR Converged Data Platform features a set of Platform Services that provide the core data handling capabilities of the platform. Modules include:

  1. MapR-FS: The NFS-mountable, distributed, high-performance MapR POSIX file system manages data storage on a massive scale.
  2. MapR-DB: The enterprise-grade, high performance, in-Hadoop NoSQL (“Not Only SQL”) database management system. It is used to add real-time, operational analytics capabilities to Hadoop. As a multi-model NoSQL database, it supports JSON document models, key-value, and wide column data models.
  3. MapR Streams: A reliable, globally scalable event streaming system that connects data producers and consumers via topics, using standard Apache Kafka APIs. 

These Platform Services are integrated into one converged data platform with file, database, and stream processing services.

The combination of these module delivers industry’s first converged data platform to deliver financial services business use cases. The platform was awarded a US patent (US9,207,930) last month which recognized MapR’s fundamental innovation in data architecture that enables real-time and mission-critical application deployments at scale. The patented MapR Platform eliminates data silos through the convergence of open source, enterprise storage, NoSQL, and event streams with unparalleled performance, data protection, disaster recovery, and multi-tenancy features.

The key components of the patent claims included:

  • An architecture based on data structures called “containers” that safeguards against data loss with optimized replication techniques and tolerance for multiple node failures in a cluster
  • Transactional read-write-update semantics with cluster-wide consistency
  • Recovery techniques which reconcile the divergence of replicated data after node failure, even while transactional updates are continuously being added
  • Update techniques that allow extreme performance and scale while supporting familiar APIs

The MapR Converged Data Platform ensures production success for Financial services organizations with an architecture designed specifically for business-critical applications and regulatory compliance applications with seamless data access and integration, and the ability to run both operational and analytical processing and applications reliably on one platform.

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