The use of behavioural analytics to identify individuals at risk of committing an offence is lagging behind the technology. It is time to understand the true value of data to financial services compliance teams, according to DST research.
We’ve all become used to receiving personalised recommendations when we go online. No matter whether you’re buying a vacuum cleaner on Amazon or listening to the latest song releases on Spotify, advertisements pop up which closely reflect products and services you have searched for in the recent past. Such is the power of behavioural analytics.
By building up this picture of behavioural patterns, often across multiple channels, analytics help companies to offer the right products and services, at the right time, through the right channels, and in the most appropriate manner.
But this technology also allows firms to predict how consumers behave, based on their actions to date. This helps them to make relatively accurate predictions about how individual people will act in the future, under certain circumstances. And, for compliance officers managing operational risks and opportunities, this is valuable insight.
Yet, a 2017 DST whitepaper, based on a study of 100 financial services professionals, about key regulatory compliance issues, and challenges, reveals that just 27% of respondents, sourced from banks, asset managers, wealth management, and insurance firms, say their data strategy has reached the delivery stage. Close to three quarters are still planning (50%) or merely thinking (23%) about their approach to extracting value from their data.
In the financial services space, the use of behavioural analytics to identify suspicious and uncharacteristic customer and employee behavioural patterns is beginning to play an important part in strengthening anti-fraud capabilities.
This means, for instance, that if a customer never uses their credit card online then suddenly shows a period of intense activity in this space, it will trigger an alert. The same technology can be used within the organisation to spot unusual employee activities, such as keeping uncommon hours or making frequent payments to the same account.
It is this last point that is of growing interest to compliance teams, especially with the advent in the UK of personal accountability under the Senior Managers Regime.
The extent of this pressure on compliance teams is highlighted by DST’s study, which showed that more than half (53%) of senior managers say the level of regulatory scrutiny has increased in the last year. DST also found that almost half (49%) of financial services firms report that they will spend more on compliance this year than in 2016 as a result.
The behaviour of the average person is influenced by a huge range of variables. Simple surveillance and modelling of human behaviour is often ineffective. Indeed, traditional model-based analytics only provides yes or no answers to specific questions.
This means conduct-related threats often remain concealed until it’s too late and regulatory violations have led to financial and reputational losses.
With financial regulatory authorities around the world demanding that organisations be equipped to uncover and tackle issues around conduct, culture, and governance on top of their normal financial regulatory activities, it has never been more important for firms to utilise every tool in their data box.
This includes analysing multiple sources of data and using deep behavioural analytics to profile individuals and spot anomalies in their ‘normal’ behaviour, so risk events are flagged before they happen.
The key to success in this area, however, is to use a wider range of data. In a trading scenario this might include orders, communications, and pre- and post-trade data.
This can be combined with supplementary data revealing overall governance strength of the organisation, the efficacy of systems and controls, company culture, remuneration packages, training, and even HR knowledge of an individual’s circumstances.
Of course, in the wider market place, compliance and risk exposures are harder to spot. When, in 2016, a leak from a legal firm in Panama disclosed more than 200,000 companies that were involved in money laundering, compliance departments were desperate to determine if their own firms were among those listed.
If a compliance team is not able to quickly access, analyse, and report on such data, possibly because that data is spread across multiple silos, it exposes the organisation to increased regulatory risk.
It is noteworthy then that in the US, the Securities and Exchange Commission's Office of Compliance Inspection and Examinations is using data analytics to identify signs of potential illegal activity.
In particular, it will focus on identifying staff at asset management firms with a track record of misconduct, and firms that have not filed the number of suspicious activity reports that would be consistent with their business models.
The advantages of a well set up compliance risk early-warning system should be readily apparent, therefore, not least in the reduction of false positives and the resultant lessening in volume of costly investigations.
But implementing surveillance and reporting solutions strung together by disjointed systems and disparate data sources is not an effective answer. Proactively managing compliance across an organisation using behavioural analytics needs data connectivity, integration tools, and processes.
These must be capable of providing a comprehensive view of as many data sources required for the automated monitoring and investigatory process as is feasible.
The answer, DST research suggests, is to build a culture of opportunity and respect throughout a business, because data touches every part of it. This makes regulatory change an opportunity to truly leverage the value of this data – a value 80% of those we asked say they can see.
And, as this 2017 study also shows that a third of financial services professionals say their organisation is not using the latest technology available to them to aid with compliance, finding a partner capable of leveraging deep financial services industry experience across technology, data, and analytics is an appropriate first step towards a better-informed approach to compliance.
It is one which moves financial services firms closer to protecting their organisation from the ever-increasing threat of damaging regulatory and reputational risk.
Managing Director Applied Analytics
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