Skip to content

Archive for

17
Jan

RegTech – Regulatory/Risk Data Management, AML, and KYC Analytics


Financial Services value chainEveryone is abundantly aware of the changing risk and regulatory landscape within the financial services industry.

Over the past seven years, we’ve seen a massive regulatory overhaul and an industry-wide push to enhance trust and confidence and encourage investor participation in the financial system.

To roadmap Wall Street regtech priorities, we have been having ongoing meetings with MDs and leading architects in global banks and investment services firms. RegTech (e.g., regulation as a service) is a subset of FinTech. Companies include

  • Fintellix offers a data analytics platform allowing banks to convert internal data into regulatory reporting formats
  • Suade offers banks “regulation as a service” interpreting real time regulatory knowledge so that banks can better manage and respond to regulation
  • Sybenetix combines machine learning with behavioral science to create a compliance and performance tool for traders

No longer business as usual.  It is clear that banks are devoting more resources to Know Your Customers (KYC),  Anti-Money Laundering (AML), fraud detection and prevention, Office of Foreign Assets Control (OFAC) compliance. FINRA is at the beginning stages of the process for building the Consolidated Audit Trail, or CAT for trading surveillance.

To enable compliance with variety of Risk/Regulatory initiatives, AML and KYC initiatives…the big RegTech related investments are:

  1. Strengthening the Golden Sources – Security Master, Account Master and Customer Master.
  2. Standardized, common global business processes, data, systems and quantitative solutions that can be leveraged and executed across geographies, products, and markets to manage delinquency exposures, and efficiently meet Regulatory requirements for Comprehensive Capital Analysis and Review  (CCAR), FDIC Reporting, Basel, and Stress Loss Testing.
  3. Various enterprise data management initiatives – Data Quality, Data Lineage, Data Lifecycle Management, Data Maturity and Enterprise Architecture procedures.

Regulatory reporting improvements via next generation Enterprise Datawarehouses (EDW) (using Oracle, IBM, NoSQL or Hadoop)– Reporting on top of EDW addresses the core problems faced by Finance, Risk and Compliance when these functions extract their own feeds of data from the product systems through which the business is conducted and use differing platforms of associated reference data in support of their reporting processes.

Lot of current investments are in the areas of Finance EDW which delivers common pool of contracts, positions and balances, organized on an enterprise wide basis and completed by anointed “gold” sources of reference data which ensure consistency and integration of information.

Crawl, walk, Run seems to be the execution game-plan as the data complexity is pretty horrendous. Take for instance, Citi alone….has approximately 200 million accounts and business in 160+ countries and jurisdictions. All risk management is made incredibly complex by the numerous banking mergers that took place over the past 3-4 decades.

Banking Mergers

Banking Mergers

 

The type of data challenges global banks like Citigroup, Goldman, Wells Fargo, Bank of America and JP MorganChase are wrestling with include: Read more »

15
Jan

Big Data Fatigue and Company Shakeout?


hype cycleBig Data is the latest “next big thing” transforming all areas of business, but amid the hype, there remains confusion about what it all means and how to create business value.

Usually when there is so much hype…there is an inevitable boom-bust-boom cycle. Hence my question:  Is the Big Data shakeout inevitable?

Are we in a big data tech bubble? If you are an enterprise customer, how do you prepare for this? What strategies do you adopt to take advantage of the situation? Can you move from lab experiments to production deployments with confidence?

The sheer number of companies that are chasing “the pot of big data gold” is astounding (see below).  While the innovation has accelerated the ability of the typical Fortune 1000 enterprise to absorb and assimilate has not. They tend to be 5-10 years behind the curve. As a result, many big data startups are either running out of cash or they are being folded by VCs into other firms.  This boom-bust cycle is a typical pattern in innovation.

http://www.bigdata-startups.com/open-source-tools/

BigDataUniverse

Source: Big Data Universe v3.. Matt Turck, Sutian Dong & FirstMark Capital

The Case of Drawn to Scale

Drawn to Scale, the four year-old startup behind Spire, shut down recently. Co-founder and CEO Bradford Stephens announced the news in a blog post. Drawn to Scale raised .93M in seed funding.

Spire is a real-time database solution for HBase that lets data scientists query Hadoop clusters using SQL. According to Stephens, the system has been by deployed by American Express, Orange Flurry, and four other companies.

Drawn to Scale showed that its technology was viable in enterprise environments and established a “presence against  competitors who raised 10-100x more cash,” but even that wasn’t enough to save the startup from its financial woes.

As Hadoop evolves and different layers of the data analytics stack get commoditized, specialized vendors like Drawn to Scale will have problems surviving.   SQL-on-Hadoop was a unique feature set…but over time it has become a must-have feature, that is becoming embedded in the stack – e.g., Impala in Cloudera CDH stack.  As a result, firms like Drawn to Scale once unique functionality becomes difficult to monetize.

Startup to Viable Ventures

The Big Data ecosystem is exploding with exciting start-ups, new divisions and new initiatives from established vendors.  Everyone wants to be the vendor/platform of choice in assisting firms deal with the data deluge (Data growth curve: Terabytes -> Petabytes -> Exabytes -> Zettabytes -> Yottabytes -> Brontobytes -> Geopbytes), translate data to information to insight, etc.

In both U.S and Europe, several billion dollars of venture money has been invested in the past three years alone in over 300+ firms.  Firms like Splunk had spectacular IPOs. Others like Cloudera and MapR have raised gobs of money. In the MongoDB space alone – a small market of less than 100M total revenue right now, over $2 Billion is said to have been invested in the past few years.

Read more »

%d bloggers like this: