Data Management

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Marty Loughlin
Marty Loughlin
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5 Ways Semantic Technology Is Transforming the Financial Services Industry

Semantic search intuitively finds and connects relevant data across the enterprise. Innovation in this technology is helping organizations simplify and transform operations.

After many years focused on reducing costs, financial service organizations are once again seeking to grow revenue. However, in the intervening years, the business environment has changed dramatically, and these organizations face significant new challenges on the journey back to growth.

First, we are in a new era of rapidly evolving regulatory oversight. Organizations must not only comply with an ever-growing list of compliance and reporting requirements, they must also testify to the quality of the data they report on. Second, savvy consumers, many of whom grew up in the age of user-friendly apps and instant data access, are demanding better service and products tailored to their individual needs.

Responding to these new challenges will require massive business and IT transformation. In particular, these organizations will need to change how they track, manage, and consume data. For many organizations, this data is not easily accessible -- it is distributed across the organization, often trapped in local business units, applications, data warehouses, spreadsheets, and documents.

Traditional technologies are struggling to address this challenge and many believe a new approach is required. Some of the new big-data solutions do help. They are good at liberating and colocating data. However, they often struggle to make it usable. Creating a "data lake" where rigid structure is not required can result in yet another silo of unusable data where context, meaning, and sources are lost. Many organizations are turning to semantic technology for the answer.

Semantic technology has been around since the late 90s but has recently gained momentum as enterprise-quality applications have emerged that make it operationally viable. Briefly, semantic technology enables data to be described, managed, and consumed in an agile, standardized, human-friendly, and machine-readable way.

While search technology allows you to find data, semantic technology enables you to find it, understand it, link it, and take action on it. It is rapidly becoming a data “power tool” for financial services, offering agility and access to data not easily available before.

Following are five ways semantic technology is simplifying and transforming operations in the financial industry.

1. Selling more products and services
For most organizations, the easiest path to new revenue is to sell more to existing customers. To sell to your customers, you must first know them -- who they are, what they buy, how they interact with you, and how they feel about your products and services.

Semantic technology unlocks and links silos of diverse customer data (accounts, transactions, interactions, and social media) to create a combined 360-degree view of customer interactions that can be used to make specific, individualized recommendations for the next best action. For example, mining call center transcripts for important life events like marriage or births and cross-referencing this information against the customer’s business interactions can be used to recommend new and relevant products.

2. Meeting regulatory reporting requirements
Regulatory reporting requirements like “Know Your Customer” are designed to help prevent identity theft, financial fraud, money laundering, and terrorist financing. These reporting needs require banks to aggregate data from disparate global systems quickly. They must also show how the data is aggregated, where it comes from, and who is accountable for it. At the same time, the reporting requirements continue to evolve, so compliance analysts need the ability to configure new views of the data rapidly, on-demand.

Semantic technology assists financial services firms in meeting these requirements by providing flexible, interactive access to data from diverse sources. Business analysts can easily add new data sources and ask new questions of the data without involvement from IT. For example, if a new country is added to the high-risk list, compliance analysts can quickly identify customers with accounts, funding sources, or connections to politically exposed persons in that country.

3. Catching insider traders
Growing numbers of high-profile insider trading stories make for sensational headlines, but also tell a story of increasing scrutiny of this type of fraud by the SEC. Detecting insider trading is challenging, requiring knowledge of what was traded, when it was traded, who traded it, what information the individuals had, who provided it, etc.

Semantic technology provides a streamlined approach to detecting and investigating adverse events across the networks of interrelated people, content, and transactions required for this kind of research and forensic investigation. An example would be connecting breaking news stories about a company to trades made in that company’s stock in the days before the news broke, identifying the traders who made these trades, and screening their email communications for related references.

4. Operational data governance
Data is big news right now as organizations seek to extract competitive advantage from this important strategic asset. But to be useful, data must be governed. Organizations must manage it, track it, and ensure its quality. Historically, data governance has been managed separately from data operations. Ultimately, this creates divergence between how we govern data and the operation systems we use to manage it.

With semantic models, we can describe data in a business-friendly way, making it easy for anyone to consume. We can also link this model, based on the meaning of data, to operational systems, bringing data governance and operations into a combined view. The semantic model of the data is always up to date and provides an easy way to see what data means and where it came from.

A business analyst can connect the data in a source system to a data warehouse within minutes rather than the months required by traditional approaches. This has benefits in customer onboarding, for example, where the ability to bring on new customers more quickly is a competitive differentiator and can lead to faster time-to-revenue.

5. Data self-service -- extracting usable business information quickly
Business users are demanding fast, interactive access to data. They want to go beyond canned reports and ask questions of their data that they did not anticipate in advance, without requiring IT to develop new data models and reports. For example, a standard report may aggregate revenue by country, but the business may decide it wants to see revenue by product within each country.

Mapping data to industry semantic models like the Financial Industry Business Ontology (FIBO) creates shared understanding, tracking, and reporting of data. Semantic models allow business analysts to see easily what valuable business data is available and to take extracts of the data for their own use in a variety of formats. For example, a portfolio manager wanting to do customized research can select just the data and companies required for local analysis, knowing it comes from a trusted enterprise source.

Leading financial organizations are turning to semantic technology to capitalize on their data opportunities. The adoption of a semantic, model-driven approach provides compelling benefits that include investment protection, flexibility, speed, agility, and business self-service. This is game-changing for financial institutions, allowing them to accelerate existing programs, create new ones not previously possible, and fully realize the value of their enterprise data.

Marty Loughlin is vice president, financial services at Cambridge Semantics Inc. Prior to joining Cambridge Semantics, Marty was the managing director for EMC's consulting business in Boston. His 25- year career has focused on helping clients leverage transformative ... View Full Bio
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MartyL919
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MartyL919,
User Rank: Apprentice
7/11/2014 | 5:39:53 PM
Re: Fraud Detection
Thank you for the feedback. Fraud is an ideal use case for semantic based analytics where uncovering networks of relationships and related activities and events can reveal suspects.
MartyL919
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MartyL919,
User Rank: Apprentice
7/11/2014 | 5:39:51 PM
Re: Fraud Detection
Thank you for the feedback. Fraud is an ideal use case for semantic based analytics where uncovering networks of relationships and related activities and events can reveal suspects.
andrewboon2739
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andrewboon2739,
User Rank: Apprentice
7/9/2014 | 10:47:36 AM
Fraud Detection

Interesting article. Fraud at any level needs to be nipped in the bud to ensure there are no losses or at minimal. Financial services can leverage data analytics which will help them in prevention of fraud. I work for McGladrey and there's a very informative whitepaper on our website that readers of this article will be interested. @ Data analytics is a powerful fraud prevention and policy enforcement tool

http://mcgladrey.com/content/mcgladrey/en_US/what-we-do/services/financial-advisory/forensic-accounting-and-fraud-investigations/data-analytics-is-a-powerful-fraud-prevention-and-policy-enforce.html



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