At the same time, insurers have also understood that they need a Big Data strategy for various purposes. Not all, however, already use tools to detect fraud.
The fight against banking and insurance crimes is a daily challenge for financial institutions around the world.
Fraud comes in many forms, from credit card scams to fake bank slips, data theft on fake websites, and irregular purchases. The fact is that during the pandemic, fraud has increased by 70%. The growth of fraud attempts has led banks and insurers to invest in anti-fraud technologies, but fraudsters are getting smarter.
For all institutions, the sophistication of this type of attack is a problem that needs to be solved efficiently.
Winning the war on fraud requires companies to outsmart criminals. The good news is that technology can help.
Thanks to Data Science, it’s now possible to improve fraud management in real-time, with more effective results and increased customer satisfaction. With data processing and analysis, Big Data, Artificial Intelligence, and Machine Learning, we can identify new attack patterns quickly.
Continue reading and understand how a Data Science strategy can help insurers avoid headaches and financial damage!
Fraud Fighting Challenges
According to a survey, the most prominent challenges institutions face in the fight against fraud are directly linked to the digital transformation that the banking and insurance sector has undergone.
The increase in the use of digital channels during 2020 has expanded the scale of fraud. Right now, banks and startups are opening accounts through apps. Not installing mechanisms to combat fraud from account inception could put future operations at risk.
These problems are not unique to the industry but are of particular concern, as fraudsters heavily target them.
The two main components in fighting fraud are detection and prevention.
Fraud detection refers to the ability to detect fraudulent events, recognize patterns, and identify if fraud has occurred.
Prevention, which is much more complicated, seeks to analyze and predict fraudulent events before they occur.
The most common moments where fraud occurs are:
• Issuing a credit card
• Financing electronics
• Buying a cell phone
• Opening a bank account
• Buying a car
• Starting a business
The main concerns are related to:
1. Data theft: Institutions are more prone to crimes based on stolen identity, and customers are more prone to scams, as personal data is used to gain company/client trust.
2. Faster Payment Processing: Shortening the time it takes to process payments poses the challenge of real-time prevention, which requires well-protected systems and automation.
3. Open banking: Data accessibility requires robust security mechanisms – such as identity verification – interconnected between various institutions.
4. Increase in digital channels: Being present on multiple channels makes the fraud prevention strategy and policy more complex. Cohesion is needed.
5. Social engineering: Scams that customers are voluntarily coerced into, such as payments or transfers to fraudsters. They are notoriously difficult to detect.
Lack of protection also brings risks to institutions, which may become legally liable for customer losses.
Furthermore, a lack of security damages credibility and the operations as a whole. Investing in prevention solutions prevents losses and criminal liability, in addition to improving the institution’s image.
Fraud Fighting Strategies and Standards
Prevention is the key to strategies against banking scams. But it’s not enough. It needs to have the tools to predict, detect, and respond to threats.
So we’re talking about a strategy that integrates data science across the institution, from tools to people, and from governance to culture.
Yes, technology is an excellent ally in ensuring that fraud monitoring is proactive rather than reactive. Allowing banking institutions to identify and anticipate fraudulent actions before they generate losses.
Imagine that the bank wants to start a relationship with a company or individual: how do you prevent fraud?
The first step is to carry out extensive research on the history of that institution or potential client, understanding their behavior.
The good news is that this process can be fully automated and executed quickly.
With a simple search on a sophisticated Big Data platform, it is possible to gather relevant information and make decisions based on that data.
The benefits of this data sweep are clear. From the data consolidated in a single report, prepared with combined criteria, managers can understand the consumer’s profile before closing the deal, validating the registration and identifying possible risk factors.
Another preventive measure available to companies is the definition of interest groups that are frequently monitored, springing to receive alerts in case of suspicious actions. There are also more advanced anti-fraud mechanisms, which we call enhanced intelligence.
In this case, an extra layer of technology is added to solutions to increase the power of analytics for decisions that come from data packs.
Personal documents offered as part of the validation process undergo rigorous verification procedures, including the use of facial recognition as proof of life.
Fraud Fighting Case Study
Denmark’s largest bank has a great example of how Artificial Intelligence and Machine Learning can provide excellent results in fraud detection.
The institution adopted a set of technologies to create and launch a fraud detection platform based on Artificial Intelligence. The solution uses Machine Learning to analyze tens of thousands of resources, monitoring millions of banking transactions online in real-time to provide insight that differentiates honest activities from fraudulent ones.
The Danish bank’s anti-fraud program is the first to put Machine Learning techniques into production while also developing deep learning models to test out strategies.
The team began work within the bank’s existing infrastructure and then created advanced Machine Learning models to detect fraud in millions of transactions per year and at peak hours.
To ensure transparency and encourage trust, the mechanism includes an interpretation layer on top of the Machine Learning models, explaining blocked activity.
The fact is, every bank needs a scalable and robust analytics platform and a roadmap and digitization strategy to bring data science into the organization.
With so many online transactions, credit cards, and mobile payments, banks demand real-time solutions to detect fraud efficiently.
AI helps uncover data ‘anomalies’ through transaction analysis and identifies fraudulent operations through data and user behavior. Machine Learning contributes its predictive capacity thanks to current technological capabilities. Rapid machine learning ‘disarms’ criminals, preventing financial theft in real-time.
This entire process takes place in a matter of minutes, sometimes seconds.
Soon after that, new fraud patterns are developed. In other words, they are short windows of action and learning to be solved by ML/AI.
The Fraud Prevention Cycle: Continuous Improvement in Defense
Data processing is at the heart of the project! Gathering, storing, structuring, and cross-checking information is the best way to detect fraud efficiently. The analysis of fraudulent behavior is crucial to the definition of a propensity indicator.
This acts as an irregularity alert to interrupt the payment process and deepen the claim analysis. For this, it is essential that managers carefully look into the monitoring and detection of threats.
As this is a continuous cycle, actions must be constant, organized, and closely monitored.
The efficient work of fraud prevention depends on the team’s analytical capacity.
Below, we have a step-by-step guide for creating a fraud prevention cycle.
To identify patterns of fraudulent behavior, companies need to process datasets – often unstructured ones.
From Data Science, it is possible to identify fraud-prone behaviors. After processing, this sea of data is organized for visualization and understanding, bringing to life sets of information in dashboards.
This is where you find out if there is fraud being committed at that time and understand the path pursued by the scammers and their strategies. Here, the organization and dashboard visualization takes place only with the information necessary for the fraud-fighting processes.
Upon finding the pattern defined by the indicator, it performs a more accurate analysis in search of irregularities that prove fraud. Digital solutions can provide detailed, real-time information for diagnoses that lead to more informed decisions.
The third stage of the fraud prevention cycle comes into play when the previously taken steps are insufficient to prevent fraudulent attacks.
In addition to reviewing the security techniques applied to prevent the recurrence of cases, it is essential to check the entire preventive process, accumulate lessons learned, and reinforce the need for policies to combat fraud in institutions.
Combating fraud deserves your attention
The message for banks and insurance companies is: invest in analytics and data technologies.
Even within a sector continuously developing ‘State of the Art’ solutions to financial crimes, a more focused look is needed for data analysis and monitoring. Advanced analytics models facilitate this process through the use of detailed customer information.
The challenge is to do this work without compromising the quality of your customer experience, which is at the heart of the strategy. One that is increasingly demanding, with intuitive, responsive, and secure solutions.
Invest in combining solutions with multiple layers of defense. Not least because, as markets become more mature from a digital perspective, threats gain new levels of complexity. Fraud has been and will increasingly become a digital arms race.
If you have any questions about preventing fraud in your company or want to boost your digital defenses, why not reach out to one of our consultants?