As published in A-Team Insight.
The dog ate my homework. The train was delayed. The postman mislaid your birthday card.
At one time or another, we’ve all used a weak excuse to forestall censure for an error of behaviour or judgement. And mostly, we’ve got away with it.
In financial regulatory compliance, however, excuses won’t wash. Especially when it comes to poor data.
“Regulators do not see data quality issues as a mitigator when they enforce sanctions and when they see issues in the compliance initiatives,” says Héctor Córdova, global director, Data Quality Center of Excellence at Innovative Systems’ compliance technology unit FinScan.
Low Tolerance
Regulators are as tech-savvy as institutions and increasingly data-focussed. Their systems are as dependent on good quality data as those of the companies they oversee. Consequently, they have a very low tolerance for incomplete disclosures.
And yet, according to a FinScan survey, there are many companies that might justifiably fall back on the data quality excuse for compliance failures. The study of 162 compliance professionals around the world said their biggest challenge to meeting regulatory obligations was data quality.
Another FinScan poll identified incomplete customer data as the biggest cause of false positives among 54% of 194 respondents, while more than half over 300 North American anti-money laundering professionals told another survey that they spent a large amount of time in 2023 on data quality.
Compliance Expertise
Córdova’s team has placed itself at the intersection of data and compliance, heading the part of the company that gets clients’ data in order before it flows into compliance teams’ systems.
“As director of the Data Quality Center of Excellence, the idea of that role is to become a hub for integrating our data quality and compliance offerings, leveraging everything that we know,” he tells Data Management Insight.
What “we know” equates to one of the largest troves of compliance data expertise among technology providers, built up over more than half a decade since Innovative Systems was founded in 1968. Such is its leading position in the space that FinScan counts among its clients institutions that include Saxo Bank, Banca Mifel, Close Brothers, Broadridge and Trium Holdings.
Mounting Concern
Organisations are recognising the need for better quality data as regulatory obligations mount. Many, however, are finding that they lack the necessary management processes to ensure their data is of a high enough standard to satisfy regulators.
A survey by S&P Global Market Intelligence late last year found that 40 per cent of respondents said they still relied on manual processes to adapt to new regulations, increasing the amount of effort and time spent on compliance processes. The same proportion said they are spending more than 10 hours a week resolving pairing and matching breaks, and managing reconciliation processes within their data. Consequently, two-thirds said they were seeking third-party solutions, leading to a decline in the use of on-premise solutions.
Sat in Silos
Through its Enlighten module, FinScan offers clients as full data-quality assurance service, putting the company’s decades of acquired knowledge into cleaning data ready for use in name, customer and transaction screening among a welter of compliance processes.
Enlighten recognises that many customers – the lion share of which are financial institutions and financial services companies – maintain fragmented data setups that has led to duplication, missing governance and standards, corrupted metadata and other serious data-quality shortcomings. This, says Córdova, a 30-year computer science veteran, produces “hidden risks” in the data. There are numerous reasons why this happens.
“When a company is acquired, you don’t know what you’re getting in terms of customer data,” he explains, citing one example. “So that can create a whole new need to take a look at that data and the quality as they merge.”
The challenge is acute in insurance companies, he says, some of which have been found to have duplication rates of as much as 90 per cent, imposing a huge and unnecessary data servicing cost on those organisations.
“Most of our customers already have centralised customer-centric platforms, but they acquire companies, they migrate, they modernise, and of course, in compliance, you need to get data from all these different platforms.
“So we still see silos. We see data with different standards, or with a lack of standards, a lack of documentation, missing governance, or very, very small governance, and documentation metadata. There’s huge amounts of work just to identify what data needs to be screened for compliance, and what pieces are relevant for your know-your-customer initiatives.”
Golden Data
Through the Enlighten module, FinScan carries out a broad assessment of clients’ data quality to identify shortcomings and pinpoint solutions.
These can include matching capabilities to unify duplicated and dislocated metadata on everything from individuals to households and companies. It also supports profiling, data cleansing, address validation, geocoding matching and data enrichment. Further, Enlighten provides a “a customer 360 platform” to help clients create “golden customer” profiles.
Clients can either use the tools within their own systems or FinScan can carry out those services for them, also hosting the necessary data.
Among its clients, those in the financial sector have particular and wide-ranging challenges, principally because they are more subject to “modernisation, acquisitions, migrations and huge data volumes”, all of which create complexity in their data pools, says Córdova. Data accuracy is also crucial to their operations, adding to the importance of getting their data management processes right.
Through its services, Córdova says Enlighten helps clients become self-sufficient on their broader data management strategies.
“We help with ‘data democratisation,’ because at the point we do profiling and when we expose the issues for compliance, people start understanding their data,” he says.
“Before that, they didn’t understand, they just saw the alerts, and so on. Now they’re able to understand the data.”