What is fraud detection?

Fraud detection and prevention are essential services that use artificial intelligence (AI) and machine learning (ML) to identify possible fraud instances before they occur.

In its early days, fraud detection and prevention focused on examining historical data and had experts manually scouring through layers of records to spot fraudulent behavior. But, in a world that largely functions online, fraud detection and prevention have become about stopping fraud instantly and preventing it from happening at all. Today fraud management solutions are automated and spot unusual patterns of activity, flagging potentially fraudulent behavior and stopping a transaction from completing.

Big problems need big solutions

The main challenge with fraud detection and its prevention is the quantum of data that organizations need to process when monitoring fraud in real-time. With the massive flow of data, you have seconds to flag a potentially fraudulent activity and stop it from occurring, while monitoring other data. Fraud solutions have to be extremely powerful to handle this data deluge. Even with a 99.7 percent guarantee of detection and prevention, organizations are still looking at 0.3 percent of fraudulent activity slipping under the radar, leading to huge financial losses.

Fraud, in itself, has several definitions and covers many criminal activities. These include theft, embezzling, and larceny too. Such acts can be committed by people within and outside the organization.

Fraud is a trillion-dollar business across almost every industry. Fraud can take place in almost every industry and has become commonplace and progressively complicated. Most industries struggle to implement the right detection systems. Traditional detection systems no longer work as fraudulent activity becomes a sophisticated, complex, cross-channel process.

Earlier businesses would be safe by running their detection processes at the end of the workday. Today, with the increase in the number of channels for a transaction--phones, computers, laptops, and other personal devices--this is extremely difficult. Businesses need superior, real-time fraud detection solutions that can detect unusual behavior across channels and update itself as well. This reduces dependence on large teams of expensive data scientists.

Digitalization and the Internet of Things (IoT) are key factors pushing the market, increasing the need for digital fraud detection and prevention exponentially. Along with the adoption of fraud detection and prevention protocols, the use of advanced analytics and its integration with AI, ML, and big data technologies is opening up several more opportunities for vendors.

Who uses fraud prevention

Many businesses have implemented a range of technologies such as data visualization (DV) and AI to reduce the financial impact of fraud. Specialized analysts and investigators work in tandem to break down information silos, rank and prioritize alerts based on severity, and channel high-priority alerts for further scrutiny.

The banking sector

In the banking system, fraud can occur in a range of scenarios, including stolen identities, hacked customer accounts, or false applications. Several other financial crimes can occur related to digital payments, authentication, and procurement. Financial institutions use fraud detection solutions to find fraudulent transactions in real-time. Banks prefer solutions that provide fewer false positives, especially those that detect trails in money laundering with complex algorithms.

The insurance sector

The insurance sector has rampant fraudulent claims and applications, with billions of dollars worth of losses annually. Rather than take the standard approach of pay first and then chase the client, data analysts are trying to prevent fraud with algorithms. ML helps them detect anomalies and patterns based on the analysis of several factors to determine potential fraud.

In the public sector

Governments use fraud detection and prevention solutions to combine data in silos to detect tax fraud, predict possible intrusions, and identify any form of irregular network behavior. Any such threats are shut down in real-time and future threats are kept at bay. Such detection and prevention protects borders, enables intelligence, promotes law enforcement, monitors illegal trade, and keeps the public safe.

In healthcare

Healthcare fraud costs the industry billions each year. By adopting the enterprise approach to ensure payment integrity, hospitals can prevent fraud. The cost of healthcare to providers is reduced with the use of advanced analytics that closes loopholes.

Who is responsible for fraud detection and prevention?

Businesses cannot underestimate the importance of fraud detection and prevention. For any business, the first logical step is to determine which department is in charge. There are two schools of thought on this.

One argument is that it should be the management because they are in charge of the daily functioning of a business. They are also responsible for creating and enforcing controls and have the necessary authority over people, systems, and information. Management also holds the knowledge and authority necessary to make changes immediately.

The other school of thought believes that the auditing department should be responsible for fraud detection and prevention. This is because they have the necessary expertise to evaluate and design controls. They also constantly review operational protocols. They are in charge of due diligence, which is essential to fraud prevention.

Both schools of thought are right because management and auditors need to work together for the best outcomes. Internal controls alone cannot help keep fraud at bay. Thought processes have to translate throughout corporate culture, management attitude, and employee training.

Most auditors feel that influencing corporate culture is beyond their realm of expertise. However, it is the auditing department that can alert management of the various risks of fraud. Management in turn can inform employees of breaches to help them better understand fraud issues. All management goals can be vetted by the auditing department. As a whole, creating and maintaining a fraud-resistant culture is a joint effort.

With guidance from the auditing department, company management can create:

Fraud awareness training modules

Training modules focus on employees and their role in fraud prevention and detection. This module is generally tied in with a corporate ethics program, creating a strong foundation for employee behavior.

Corporate fraud policy

Corporate fraud policies outline what employees need to do when they suspect fraud. This helps create a clear course of action on how a company handles fraud attempts. The policy should send the message out that no one can commit an illegal act against the company.

How does fraud prevention work?

Fraud prevention strategies are not static as there is no specific start or end to the process. It is a continuous process that comprises tracking, analyses, related decision-making, case management, and the ability to feed analysis results back into the system. Each fraud is a learning opportunity for organizations, and these lessons can be incorporated into newer monitoring and detection procedures.

Fraud can comprise a large variety of things--from corrupt management and staff to mismanagement, illegal transactions, money laundering, terrorist funding, embezzlement, and threats to public security. The identification of fraud, its enforcement, or protection must be the primary goal of the organization. With next-generation technologies, AI can automate large data sets and run analyses. However, fraudsters use this same technology to enhance their skills, so detection methods are constantly evolving.

Challenges in implementation

As with any protocol, there are challenges to managing fraud detection and prevention. For any company, their work is geared towards medium-to-long term goals. Every organization tries to achieve what is known as the scissor effect--where risks are reduced as corporate income rises. This is not a simple thing to achieve, but doing it successfully can contribute immensely to a company’s growth plan. To achieve the ideal anti-fraud system, organizations should focus on utilizing skills, processes, and systems. A sound strategy can be built on a foundation of four important pillars:

Pillar #1 - prevention

Companies should ensure there is a constant assessment of fraud risk and create a system of process audits, especially for vulnerable income streams. Management and audit teams will have to make robust anti-fraud policies and introduce an annual fraud awareness program for employees. Due diligence prevents the vulnerabilities of human error.

Fraud prevention includes due diligence in the form of Know Your Customer (KYC) and Know Your Partners (KYP) systems. Companies should collaborate with specialized firms that can provide knowledge on fraud market intelligence. Many industry-specific forums offer such consultation services. A fraudster behavior intelligence (FBI) system profiles techniques used by fraudsters through a fraud case intelligence (FCI) that documents and shares learning in an organized case:

  1. Case opens
  2. Case is evaluated and assessed
  3. Case is processed and action is taken
  4. Case is closed and the closing conditions are documented
  5. Case frequency and impact are assessed

Pillar #2 – prediction

Perpetrators of fraud are constantly strategizing on how to exploit loopholes or weaknesses in organizations.

To counter this, companies need to create learning systems based on predictive techniques and data mining. These techniques help identify fraudster operations and prevent false positives in learning systems. Prediction can never be 100 percent accurate, but it can:

  • Set company targets
  • Plan for deviations from the target
  • Enable corrective action where needed
  • Incorporate into the business plan
  • Measure the efficacy of risk reduction

With prediction, a company can work towards achieving the scissor effect. Organizations can develop their forecasting methods to detect fraud as well.

Pillar #3 - detection

Despite all the measures to prevent fraud, there is a chance for some attempts to slip through the cracks. In such cases, early detection can reduce or negate any impact. There are several methods, technologies, and procedures that can mitigate the damage. An advantage of quick detection is that it acts as a deterrent to potential fraudsters and makes the anti-fraud stance of the company stronger. Detection includes:

  • A hotline for whistle-blowers, internal or external
  • Online and offline systems of detection
  • By the law interceptions of fraud attempts and subsequent investigations
  • Internal and external tip-offs
  • Internal and external audits
  • Corporate security measures
  • Risk management measures
  • AI and ML to identify fraud in real-time

The right technology mix is essential to supporting fraud detection. A collaborative architecture has to provide an all-round view of the organization’s systems. Everything from fraud management systems, call tracking, data mining, and predictive analysis has to be taken into account. Successful detection architecture is built on:

  • Strong expertise to lessen false positives
  • Regular forensic and root-cause analyses
  • Investigation and implementation of dynamic controls

Pillar #4 - collection

This is possibly the most difficult pillar--getting your money back. In cases where fraudsters have been caught and are being tried in a court of law, having all the necessary evidence can help a business recover. Companies should:

  • Build a legally-binding means of value on the losses incurred
  • Examine the penalties laid out by law
  • Seize of the fraudster’s property, where the law allows
  • Develop a penalty for those who may have assisted in the commission of fraud

The possibility of fraud is inevitable for all businesses. However, its detection and prevention is something an organization can work towards diligently. The right expertise and technology mix can make all the difference in safeguarding a company.

Fraud Detection Diagram