Finance Focus Dave Excell Ian Felice
Chief Technology Officer Featurespace
Tel - +(350) 200 79000 Fax - +(350) 200 71966 Email -
ian.felice@hassans.gi Website -
www.gibraltarlaw.com
Why Risk Management Doesn’t Have to Mean Risk Aversion
With the UK in the middle of one of the most prolonged periods of economic depression for 150 years and the financial sector still feeling the impact of the 2008 crisis, retail banks in the UK have been keen to re-iterate their commitment to business. Yet, many are being constrained by a conservative perception of risk, which is making them unwilling to lend to enterprises.
Mike Cherry, national policy chairman of the Federation of Small Businesses (FSB), small businesses are being turned down for lending because banks are risk-averse in the current climate. "We've come across business after business who are saying that they were not able to get the finance that they desperately need to grow their businesses."
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In many banks, and indeed in other financial services organisations, we are seeing a battle emerging between the marketing teams and the risk management teams who are often at odds in the way they manage risk. The marketers want to on-board as many converted prospects as possible for maximum revenues and campaign ROI. Conversely, risk managers want to weed out ‘grey risk’ candidates to minimise any possibility for negative outcome once the customer is live in the system.
Currently, with the emphasis squarely on risk aversion it is the risk managers that are winning the argument.
Sticking to the Rules Making the matter yet more complex is the strictly rules-based approach to risk management employed by many retail banks: blocking financial transactions outside the customer’s home country, for example, or outlawing transactions over a specific monetary value. Such an approach is ultimately far too rigid and inflexible for retail banks to follow without significantly inhibiting potential growth opportunities.
Fortunately today, driven by the latest next 108
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his is significantly impacting the health of the UK economy by dampening down prospects for growth, particularly across the small business sector. According to
generation technology, a new approach is emerging which allows banks to make educated predictions about likely outcomes based on statistically valid analysis rather than exact decisions based on flawed models. Adopting this approach gives banks the flexibility to lend more money and pursue growth while at the same time managing risk more effectively.
Finding a Solution Any technology solution that is applied here needs to be able to manage a delicate balancing act finding a way to enable banks to take on more business and therefore potential risk while at the same time having greater confidence in their ability to more accurately identify and intercept real problems at source. Real-time capability is key here. To be effective, solutions applied to this challenge have to be able to automatically process thousands of pieces of data in real time, to allow companies to identify potential risk and make instant and objective decisions about whether or not to take on new business.
The concept of behavioural change recognition can also be key. To take a common example, a bank is considering loaning money but wants to ensure applications from a particular region are not fraudulent, due to identity theft. In such situations, fraudsters make multiple applications using the same or similar sort codes or postcodes.
However, it is possible that localised issues could cause such spikes in applications – for example, the failure of a local employer to pay salaries on time. Therefore it is important to determine whether a sort code is simply increasing in frequency or whether it indicates a fraudulent attack, to ensure valid applications are not unnecessarily delayed and customer satisfaction is maintained.
Unfortunately, rules-based systems – the usual approach for such protection – would struggle to cope here and would typically require extensive rule creation, monitoring and tuning.
By applying the latest behavioural change recognition techniques, however, technology providers can give lenders the ability to monitor their high volume, real-time loan application data stream to look for unusual frequencies in the field responses. They can do this by using both the context of an individual’s application and the global context of all applications to evaluate a risk score for each anomaly, instantly generating alerts for the review team when potential threats are identified.
Learning from Experience Critically too, banks real-time self-learning capability can be employed to instantly block repeat fraud if discovered, which means that retail banks are able to loosen acceptance criteria. In essence, on-boarding teams can accept the grey risk candidates, safe in the knowledge that any genuine alerts will be easily manageable thanks to significant false-positives reduction.
Equally, by delivering this capability, banks can strike that all important balance between the approaches favoured by their marketing and risk management teams and ensure they keep both groups equally happy.
In July 2012, an international trade report by the Institute of Export and Trade found that inadequate access to finance remains one of the biggest barriers to business growth. Initiatives such as the Enterprise Finance Guarantee and Project Merlin have not helped to force banks to loosen lending criteria. At the level of the individual retail bank, at the very least, real-time behavioural change recognition driving enhanced risk management provides a much more promising solution.
Partner- Hassans & CEO- Line Management Services Limited 57/63 Line Wall Road, Gibraltar
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