Circumventing Fraud Trends in 2018
12 April 2018
Businesses constantly need to consider new ways of protecting their systems to combat fraud. For example, implementing machine learning is a viable option that will continue to increase revenue while protecting the consumer base. Conquering the old trends while understanding the new trends will assist businesses in successfully combating cyberfraud throughout the year. These trends are high on the priority list, and dominate the forefront this year:
Knowing the right buzzwords can keep your company out of hot water. There are a number of regulatory changes coming this year, and knowing words such as AI, multi-factor authentication (MFA), and machine learning can work to your advantage.
Integrate data science
You must know what you are dealing with before you can take the steps needed to circumvent the issue. Financial services must use data science when implementing systems to protect their consumers. Assess the customer journey in real time to identify any unusual actions or activity. Using a self-learning system can assist with avoiding new fraud mechanisms.
Don't forget old tricks
Although there are many new tactics, it is important to remember the tricks that have previously worked. For instance, social engineering is still very much alive and well.
Finding and implementing a good fraud management solution that will work with your systems and protocols can make the difference. Machine learning systems are definitely the way to go, working to spot behaviors indicative of fraud. This helps deter fraudsters from the very beginning. The software in a machine learning system understands individual behavior in real-time scenarios, keeping costs down for operational systems.
- Real time fraud -- These systems are designed to detect fraud as it occurs. There will be no more instances of finding out about breaches weeks after it happens. The team will be immediately alerted to any variances going on to block fraud attempts.
- Reduction of errors -- When the thought of fraud occurs, real customers may inadvertently become blocked by mistake. These systems use adaptive behavioral analytics to help teams detect fraud attempts or inconsistencies in the system. This reduces the number of blocked customers.
- Improve accuracy of fraud models -- Fraud models continue to improve over time when utilizing these types of systems, working to the advantage of the company, and not against it.
The more organizations become involved in learning management systems, the greater the chances are of protecting sensitive data and operations. Keeping these protocols in-house could aid in the reduction of occurrences while keeping costs low if implemented correctly. Recognizing the importance of integrating a self-learning system, and the risks management will take when not being proactive can work against the company. Working toward a greater outcome against fraud risks is beneficial to the company, in 2018 and beyond.