CONDUCT RISK SOLUTIONS
Conduct risk is a priority for banks. Daniel Melo, Senior Director for Fair Isaac Advisors, FICO’s consulting practice answers our questions about conduct risk and discuss how models and systems designed to address conduct risk can help identify and measure other operational risks while improving business efficiency and increasing the return on your investment.
Please give a brief description of conduct risk and its meaning to the layman.
Conduct risk usually refers to the risk a bank’s misconduct poses to a bank’s customers, but the risk is also there for shareholders and the bank itself. When a bank has inappropriate policies, or policies that are not followed by its employees, this can result in large losses, fines and inappropriate charges. You can read about conduct risk nearly every day in the UK, where many of the top banks have been tarnished for PPI misselling, LIBOR fixing and other bad practices. This has led the Financial Conduct Authority to tackle conduct risk head on.
With so many potential implications for customer satisfaction, organisational reputation and regulatory compliance, every financial institution, its organizations and employees, need to put conduct risk in the center of its radar. In some cases, it may mean the introduction of new policies or processes that could require significant cultural change within the organisation.
Given the problems banks are facing in other markets, you’d think conduct risk would be a universal focus. But it isn’t. Outside the UK, the term is rare, and fewer banks are focused on creating a systematic way to prevent the kind of fraud, waste and abuse that leads to catastrophic fines, losses, lawsuits and reputational damage.
How can using a systemised approach in addressing conduct risk benefit both the customer’s experience as well as the efficiency of the business employing the system?
Banks have started to tackle conduct risk mitigation by assessing priority areas and reinforcing guidelines for conduct. But most have not yet determined how to operationalize conduct risk mitigation into everyday decisions and systems. It’s one thing to tell people to behave and follow procedures, but how do you make sure they’re doing that? How quickly can you identify a problem?
To provide excellent service and a fantastic customer experience, all financial institutions must manage their conduct to reduce or mitigate the risk of errors and mistakes. The advantage of a system for managing conduct risk is that it enables you to monitor activity at the right level of granularity, automatically detect when a certain behavior or action falls outside established parameters, and immediately create a case to correct the behavior. Banks use this kind of automation in all their customer decisions — it has equal value for monitoring and guiding internal actions.
Systematizing conduct risk management also enables you to identify areas that need improvements. Are there gaps exposing the institution to claims of wrongdoing? Could one mistake lead to more and make the problem bigger? Is it worthwhile taking action to change the process (balance between risk and reward)? Can you identify any priorities that would justify the investment?
Please explain how, by adopting an analytics-based system, banks can restore trust in both their consumers and the regulatory bodies they are aligned to.
The adoption of analytics restores trust by enabling the prediction of breaches and the prescription of actions to reduce and/or eliminate breaches. The combination of predictive and prescriptive analytics with continuous learning loops enables a systematic approach to Decision Management capable of significantly increasing the adaptive agility of an enterprise, as well as the quality of its operations.
Building conduct risk measures into each stage of the customer lifecycle as needed—making it part of day-to-day operations—can help ensure that the right steps are taken as a matter of course, lessening the burden on employees and ensuring every customer interaction is watertight. An analytics-based system works by encoding a bank’s rules and parameters right into the bank’s operations. It’s a way of ensuring that the correct actions are taken, and that any actions that appear to be outside the bank’s standards are flagged – much as the bank would flag a credit card transaction that looks suspicious.
What we tend to hear when banks encounter conduct risk issues is “We didn’t know this was happening.” When you have the right system in place, that won’t be an issue.
In what ways does data-driven analytics provide a clearer insight to tell a bank where their next conduct risk exposure may come from?
Many banks have traditionally taken an expert-led approach to compliance and risk management, relying on individuals to identify trends and predict customer behaviour. However, these analyses are often subjectively biased and do not always provide a clear measure of risk. In addition, manual assessments can quickly become overwhelmed when faced with large account and customer volumes.
While expert opinion is essential when designing the policies and processes that address both business opportunities and conduct risk responsibilities, it is data-driven analytics that can provide the clearer, deeper insight that will tell you where your next conduct risk exposure is coming from.
With this understanding, you can make more accurate decisions about what policies and processes you need to implement and more effectively measure their impact. Analytics-based Link Analysis is also critical in spotting chains of error or malpractice. While an individual instance of customer detriment is of course undesirable, when repeated it becomes part of a wider pattern or habit among your employees that could create a multifaceted and significant conduct risk exposure. Being unable to spot these connections could leave your organisation vulnerable.
For example, say staff at a particular branch are trying to boost their monthly sales figures by selling high-value long-term savings products to customers in their 80s. While the sales may make the branch staff happy and add to the bank’s revenue, the products sold are not appropriate to the target group or in the customers’ best interest. They could result in a significant compensation bill, not to mention reputational damage for the bank, should consumers and regulators become aware of the practice.
Using tailored analytic models across all sales activity and customer interactions will enable you to predict where your conduct risk efforts should be focused. We help our customers harness decision models that utilise a vast range of conduct risk inputs and other variables to identify decision strategies that balance profit, regulation and customer satisfaction.
FICO provide many solutions to address the problem, are there any ways in which a bank can use an analytics-driven framework to manage their conduct risk which will turn it into a competitive business advantage?
We believe there is a significant first-mover advantage, and those banks that take proactive action now have a lot to gain. Not only will they avoid penalties from the regulator and compensation payouts to customers, but they will also create proof that they are living up to their customer charter.
More transparent customer service and fairer treatment are what many consumers are demanding—and the ones that get it are likely to respond with increased loyalty. Combating conduct risk is an undeniable challenge, but the tools to meet it are at hand. A fully operationalised, analytics-driven framework will help harness and manage your conduct risk exposure and turn it into one of your greatest competitive advantages.
What new innovations is FICO working on over the course of 2014?
FICO’s innovation imperative is to enable Analytics-driven solutions by aggregating all of the necessary capabilities, and then, connecting them. FICO subscribes to the notion that the mitigation of conduct risk requires a systematic approach.
Customers implementing a conduct risk solution must consider
1) The management and integration of the rapidly growing sources of structured and unstructured data
2) The integration of typically disjointed enterprise processes
3) The deployment and proactive management of models
4) The deployment of persistent feedback loops
5) The deployment of actionable performance dashboards, and last but most importantly
6) The deployment of a cohesive and agile decisioning capabilities.
In 2014, FICO is introducing a number of new capabilities organized in a complete and fully integrated Stack designed to solve for all of these challenges. The FICO Decision Management Platform is at the center of the Stack. It delivers a real time, Analytics-driven decisioning engine complemented by modeling, rules management, and optimization capabilities. Complementing the FICO Decision Management Platform, the company will also introduce the FICO Analytic Cloud, a first of its kind. Not only will it deliver the power of FICO’s Decision Management Stack, it will also serve as the collaboration hub for data scientists, analysts, developers, and integrators to solve for fundamental business challenges, such as conduct risk.