Across the financial services industry, banks, insurers, traders and fund managers are working in rapidly changing environments driven by post-crisis regulation and balance sheet pressure. At the heart of this is a significant and complex data challenge. Jarred McGinnis, UK managing consultant, Ontotext, looks at why the industry should turn to semantic technology to tackle these issues.

Financial Services is arguably the most data-dependant industry in the UK. Everything from market prices, risk management, liquidity measures and payments happen at phenomenal speeds and massive volumes. Factor in compliance demands to report all of this operations data, both internally and externally, and the data created by other internal activities and those with the vision and imagination might be able to comprehend the magnitude of the challenge.

A key feature of this challenge is the finite processing capacity of a human being. Even with the sharpest minds, educated to the highest standards, no human can broach data in financial services the way a computer can. So globalised is the network of information required for financial services operations, computational power is required to aggregate all of the required information for a human on the receiving end to then interpret it.

Certainly in the last few years there has been no shortage of technology providers heralding their big data tools as the ‘must have’ for any functioning institution. With all the noise being made, how is a CTO supposed to know what technology suits both their needs and their legacy systems? Anyone close to the industry knows that technology is supposed to be fighting the dual battles for firms to both be compliant and drive profitability. Yet conversely, many institutions have had IT budgets cut as they try to respond to volatile market conditions.

This is where semantic technology can benefit an industry trying to grapple with its regulatory and technical issues. Semantics and graph databases have applications that can simultaneously benefit compliance teams and revenue driving teams. Semantic technology removes ‘signal noise’ and presents data in a way a human can understand, rather than regurgitating how the computer organises and processes, improving the clarity of reports and information.

What does semantics mean?

So pervasive is the marketing jargon used by technology firms, understanding if a technology will meet your needs can be confusing for IT teams.  Therefore I believe it to be pertinent to explain what the use of semantic technology can mean for the financial institutions that use it.

For compliance teams, semantic technology can identify the links between all of the different businesses and entities that they are reporting on. Whereas a typical data analytics tool will be able to match text together – thus identifying all documents that reference ‘Company A’, semantics can go deeper into the data store and deliver much richer returns.

With semantic technology, compliance teams would be able to identify not just everything pertaining to ‘Company A’, but would also be presented with subsidiary’s B, C and D which are owned by, or affiliated to, Company A and the liabilities that each subsidiary has in their respective jurisdictions.

Thinking back to the financial crisis, the complexity of derivative assets meant that ownership of said assets was incredibly opaque. Here, semantics can have a very important application towards risk profiling. Semantic technology has the ability to draw out the relationships within data which could match the single assets from within the bundled packages and identify who owns what. This would mean that in instances such as the previous crisis, although not completely avoidable, it certainly becomes easier to respond to.

Add to these examples the benefits for legal teams and investors knowing the breadth and similarities of patent filings, or insurance underwriters searching and extracting real macroeconomic conditions for policies and it becomes evident how the entire spectrum of financial services can benefit.

In this way semantics allow for a vision of financial products in a block-by-block way, in essence creating Lego out of these opaque financial vehicles. Consider creating an ontology for ‘companies’ then modelling for a patent filing, you can come back to the model for companies. This removes the need for multiple systems across a firm as the model for a legal team can be applied to the securities trading team. Semantics allow for the building of a knowledge model for business needs as opposed to building new models from scratch for any given task. The ‘knowledge’ can then be applied universally throughout an organisation.

Accessing the universal strategy

The ghost of crises past still hovers over the industry, the volume, velocity and variety of skeletons in the closet which drove the most recent meltdown still feature heavily in the creation of new rules and processes today. This had led regulators and industry participants down the path of creating more openness. This journey has brought with it collaborations which will hopefully steer the industry away from the dangers of the past.

This desire for openness and collaboration has seen the Object Management Group and Enterprise Data Management (EDM) Council create the Financial Industry Business Ontology (FIBO). Simultaneously the Regulatory Oversight Committee (ROC) has created a worldwide framework of legal entity identification, The Legal Entity Identifier Regulatory Oversight Committee (LEI ROC). The Legal Entity Identifier introduces unique 20-character, alpha-numeric codes, to: ‘identify legally distinct entities that engage in financial transactions.’ The steps taken by these two bodies are introducing standards which factor in the global nature of financial services.

It is clear that semantic technology will play a large part in shaping the future across the financial services industry, be it for government agencies, banks, funds or insurers. The attraction of semantics being that it can automatically and reliably share industry concepts without ambiguity across organisations and governmental jurisdictions.

All financial services firms already hold the data that will enable them to better calculate risk, measure liquidity, understand the firms they invest in and respond quickly and accurately to compliance demands. By applying semantic technology firms from across the universe of finance services could soon improve their processes, and crucially, overall business performance.