Top 4 Technology-Driven Techniques Used For Fraud Investigation

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Top 4 Technology-Driven Techniques Used For Fraud Investigation

Experiences have shown that scammers have become very innovative and have developed a wide range of schemes to commit fraud. For this reason, many organizations have been looking for effective solutions and countermeasures to prevent this from happening. 

The ever-growing amount of data that is available today provides opportunities for technology-driven techniques aimed at detecting suspicious behavior which helps investigators to find signs of fraud they weren’t even aware existed and allows them to prevent fraudsters’ schemes from succeeding.

These technology-based techniques have become increasingly sophisticated and help organizations effectively analyze data and detect possible anomalies or patterns that may suggest fraudulent activity.

In this post, we take a look at the top 4 technology-driven techniques used in the fight against the growing risk of fraud.

Fraud Detection APIs

The cybercrime rate is high all across the world and one of the ways to help reduce it is by collecting data about its occurrences so that proper preventive measures can be taken. Preventing fraud is crucial and one of the most flexible solutions are Application Programming Interfaces (APIs) enabling businesses to get protected by allowing two systems to interact, communicate and integrate.

This should be considered by businesses to protect their corporate data and domain privacy and prevent someone from breaking into their domain control panel and transferring the site to a different name. 

Many cyber security and anti-malware companies, bank and financial institutions, government agencies, brand owners, and protection agents use WHOIS API since it contains well-parsed record data that provides information against any domain name, such as domain registration details, domain owner email address, and much more.

Artificial Intelligence

In recent times, people have turned massively to online shopping. To detect fraud in today’s dynamic corporate environment which boasts of an overwhelming amount of traffic and data can be challenging.

Cybercrime costs the global economy about $1 trillion, or slightly over 1% of global GDP, making it a serious threat to organizations and their customers. As a result, many have turned to AI to detect fraud as it has helped them to improve internal security and simplify corporate operations. AI is considered a significant tool in the fight against financial crimes due to its increased efficiency. It’s used to analyze a myriad of transactions and uncover fraud trends, which can afterward be used to detect fraud in real-time.

Once fraud is suspected, AI models can be utilized to reject transactions or flag them for further investigations, which allows investigators to focus more on the most promising instances. AI-driven technology can also absorb knowledge from investigators when they evaluate and clear dubious transactions, which in turn reinforces the AI model’s knowledge and avoids trends that don’t point to fraud.

Data Visualization

To successfully detect fraud, an investigation of a vast amount of data that stems from different anti-fraud systems with different types of data is required. All the data must be combined and statistical methods must be used to uncover suspicious claims. All this is time-consuming and most often inefficient.

However, data visualization can enhance the prompt identification of relationships and important structures, as well as the detection of suspicious patterns and quickly spot relationships that may be hidden in the data. Despite the visual exploration, the interaction with data provides a deeper understanding of the dependencies within the data altering over time.

The advantage of data visualization is that it can drastically reduce the data analysis time and speed up the fraud investigation. There is analysis software that can automatically create models and identify questionable financial transactions patterns.

With data visualization software, you can be free of data capturing, cleaning, and normalizing and focus on the data rather than developing technical expertise to create models for the investigation.

Data Analysis

Data analysis is the process of collecting and analyzing lots of data to draw conclusions about consumers’ behavior patterns, personal interests, and purchasing trends. Such analyses accelerate decision-making, improve business processes and user engagement and reduce costs, as well as increase growth and profitability.

Data analysis extends across the entire analysis landscape – from the most basic, descriptive, and diagnostic analysis to the advanced, such as predictive, normative, and computer science analysis. Advanced analysis can be used to detect fraudulent activities in real-time that should be forwarded for immediate investigation. Transactions can be stopped in parallel with an investigation being carried out.

Final Thoughts

Fraud is a constant threat in today’s society. The methods used by scammers are often very sophisticated, allowing them to scam victims with the help of an organized and well-planned approach. Scammers know how to crack the system, which has prompted organizations to consider the abovementioned technology-driven techniques to advance investigations while minimizing costs.

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