Gauging the Success of Anti-fraud Efforts
Financial crime changes daily and is a multibillion-pound industry. Financial institutions continue to develop strategies to reduce fraud and diminish the negative business impact of illegal practices that are becoming more complex and common worldwide. There are several safeguards, but most are inefficient, unsophisticated and disorganised. The bottom line is that fraud can be thwarted. But first, we have to understand it.
The various forms of fraud often result in the purchase of drugs, illegal weapons or even funding terrorist organisations. It is like a balloon: squeeze one side and the other side gets bigger. Today’s reality is a new form of capitalism built around pillage, criminality, corruption and complicity where bad or illegal decision-making, combined with insider fraud and collusion within the industry, are affecting financial institutions on a global scale.
In our current economic climate, everyone is rethinking next steps in the financial industry, and most agree that fighting financial crime requires a technology solution that provides enterprise, cross-channel fraud detection. But many struggle on just how to get there. Good fraudsters are creative, highly developed, and hit multiple channels fast. The world has not seen a more sophisticated criminal, and the velocity of financial attacks today is unparalleled. A consolidated and consistent approach provides maximum return on investment as organisations manage fewer vendor relationships and use products at the enterprise level.
Fraud detection technology has evolved and now offers toolsets with predictive analytics, advanced techniques, and the ability to ‘learn’ from experience. This ‘learning’ characteristic enables software to increase in sophistication as more intelligence is gathered over time. The more intelligent the tools, the better the chance of detecting and predicting potential risks before criminals discover an opportunity. Combining the early generation of fraud-fighting tools with advanced analytics and adaptive optimisation gives the financial services industry the opportunity to gain ground in the fraud race. But the weapons are only as good as the data. The obvious solution is an all-in-one system where the data coming in and out is the key. That data – and how an institution leverages technology to use it – will be the weapon of choice in today’s battle.
However, this is only half of the battle, once the technology and protection is in place, how can organisations go about measuring its effectiveness?
Setting goals, milestones or performance metrics are common for determining success in business. In a broad sense, a company uses a profit/loss or income statement to determine profitability or performance for a certain period of time. This same method can be applied to determine success rates for financial crime prevention.
Fraud detection and prevention metrics have been a challenge in the past. In the early 1990s, when fraud detection became a focus of the banking industry, the increasing size of general ledger accounts for charge-offs and other losses was becoming a significant contributor to overall expenses and affecting profitability. It was not uncommon for these loss accounts to contain not only fraud losses but also uncollected overdrafts and fees, bank robbery losses, teller outages and a multitude of other items. The end result was general ledger accounting that was not an appropriate or a sufficient means of tracking the detailed losses incurred by banks.
As loss numbers grew, a more detailed analysis occurred and revealed that a significant amount of these balances were in fact due to fraudulent activity of customers and third-party perpetrators. This revelation prompted the creation of fraud prevention departments who then implemented a number of procedures and software products to battle the fraud. These early initiatives focused on fraud awareness training, implementation of cheque and deposit fraud, and kiting detection software. It has evolved over the years to be a detailed scoring process used to assess success and to better understand the issues at hand. Early on, the raw amount of loss was the primary metric for success. As fraud detection and prevention matured, far more data intelligence has been developed to measure success in other ways.
Measuring success of systems and processes requires maintaining certain types of data and information. The task of optimising a system or process requires different data and a different way to look at it. The most common rationale to measure fraud is a need to know the extent of the problem to effectively solve it. Without knowing the size and scope of the problem, how do we know where to deploy resources? The scarcity of money and staff to combat fraud suggests that efficient allocation of resources is vital in order to effectively deter and detect fraud. Understanding the characteristics of the fraud is critical to make the correct decisions regarding the selection and calibration of software solutions. A consistent measurement system is needed to understand the degree of impact various solutions have on the problem. This is especially true in determining whether anti-fraud activities have any impact on deterrence, which may be even harder to measure than the extent of the problem. Another important reason for consistent measurement is public credibility. Consumers are more likely to buy into solutions if they are convinced a problem exists and will directly affect them.
Companies also must address false positives and false negatives. False positives are those transactions identified by a particular fraud detection software that upon further review turn out to be legitimate transactions that posed no threat. The goal generally is to minimise the number of false positives without affecting detection and prevention. Although not a risk to the bank, false positives are a hindrance resulting in lack of productivity. False negatives are those transactions or activities that are truly fraudulent that went undetected by the system or process and resulted in a loss to the organisation. Overall, an organisation wants to find the optimal position where false positives and false negatives are minimised and the cost of reaching that position creates an appropriate business case and return on the investment made by the organisation.
In today’s economic environment, senior management remains under increasing pressure to produce results in the fight against fraud. Financial institutions need to take a comprehensive look at tools and methodologies to improve efficiency and better understand the fraud environment.
Measuring fraud will never be easy and likely will remain controversial. Reaching consensus on definitions and methods will be difficult, especially when people focus narrowly on their own operations instead of keeping the big picture in mind. But such consensus is essential if effective measurement programmes are to help spotlight damage caused by crime and ultimately convince the public and decision-makers how much fraud affects the economy and our lives.