Data security

How Artificial Intelligence can Transform the Fate of Fraud Detection

The world has made phenomenal discoveries in the field of science and technology. But it is the disruptive innovations that have changed our lives the most. Artificial intelligence is one such disruptive technology whose applications have overwhelmed the human perception. From bringing more efficiency to our day to day tasks to helping doctors make better diagnoses, AI has penetrated every walk of human life. Fraud detection is one of the many applications of AI that have allowed banks and e-retail businesses to manage the threat of identity theft and credit card fraud.

With the transformation of customer buying behaviours and the accessibility to the internet, the volume of transactions has increased exponentially. Moreover, online payment methods and non-cash transactions have made the task of managing fraud even more challenging. Simultaneously, cybercriminals and fraudsters have found more refined ways to bypass security systems embedded in websites. This has compounded the task of identifying fraud for companies, let alone preventing it in the first place.

Enter the role of artificial intelligence and machine learning. Computer scientists have been devising tools that can transform the future of fraud prevention. Digitised solutions have been introduced in the market that can make it easier for banks and online businesses to identify fraudsters and thwart them before they can do much harm.

Existing Tools for Fraud Detection

At present, at an institutional level, the systems used for fraud detection are either manual, primitive, or both. With the volume of transactional data coming in through an average business’s channels, it is insane to think that it can be evaluated manually. Some sort of legacy and rule-based systems are implemented by even the most old fashioned companies. Outdated transaction review systems employ fixed rules to screen transactions and issue alerts. Those alerts are then further reviewed manually. However, with the complexities that have proliferated in cybercrime and online fraud, fixed rule-based systems cannot complex fraud activities. There has to be a better solution to detect a fraudulent transaction.

Even the customer verification systems are slow and inefficient. For the most part, customer verifications performed by banks and businesses are manual. They take time and need vigilance on part of the person responsible for verifying the customer. Despite that, scammers manage to deceive attempts for customer authentications.

Rule-based transaction monitoring systems and manual user verification methods can slow down the transaction processes for customers. This may end up frustrating them to abandon their purchase altogether. Most such systems also produce an irritating amount of false positives for human officers to investigate.

Added to this is the cost of fraud management. Companies invest resources exhaustively to implement fraud protection to their online payment channels. They may also spend graciously on human capital to hire and train them to detect any fraudulent activity. Still, cybercriminals manage to bypass them, rendering all such systems ineffective. This desperately calls for the need for an automated, smart, and cost-effective solution to facilitate the whole process.

AI-Based Solutions for Fraud Detection

The breakthroughs in the FinTech industry has led data scientists to devise fraud management tools based on AI and machine learning technology. AI uses adaptive protocols to perform normal tasks in a smarter way. Machine learning is further a division of AI that uses complex algorithms for predictive analysis. It works with large and complex data sets to reciprocate to situations that have not been programmed into it. Its various applications involve predictive analysis, image recognition, spam detection and product recommendations. All such solutions can reduce the time it takes for humans to perform fraud detection among other tasks.

Predictive Analysis

Advanced algorithmic methods, when implemented to consumer data, can improve the process of fraud detection. AI and machine learning can sift through complex looking data within minutes and detect suspicious behaviour that would otherwise have been missed by human intelligence or primitive methods of monitoring. It can also issue timely alerts and suggest appropriate actions for managers to take in case fraud is detected. An ML fraud management system can continuously add more information to its algorithm to improve its protocols for better accuracy.

Consumer Verifications through AI

Another AI-based system for fraud prevention is of online customer identification and authentication. Such systems use digital verification methods like document scans through OCR (Optical Character Recognition), facial recognition software and AML (anti-money laundering) checks. All such verification methods are used to weed out any bad actors and serious scammers. Authenticating customers’ identities through AI software can help businesses keep out any threat of identity theft and credit card fraud. Identity proofing of customers can be performed in real time through AI-based protocols.

Why Should AI be Used for Fraud Detection?

Cybercrimes and fraud have long vexed businesses in every industry. They are practically losing billions every year due to fraud. If technologies like AI and machine learning can help with fraud detection and enable businesses to carry out their operations in a safer manner, then systems and tools should be implemented to do so. Some of the most convincing reasons for businesses to use artificial intelligence for fraud management include;

  • Speed: In a world of increased digitisation people prefer their tasks to be easier and faster to perform. Legacy fraud prevention systems and manual procedures can take a long time to perform. Hence the need for faster, automated solutions. For businesses to improve the pace of their fraud management, AI-based solutions have presented the perfect opportunity. Machine learning tools can now analyse client data with minutes to look for any suspicious transactions and detect anomalies. They can evaluate large amounts of data with speed and accuracy. Client verifications through AI systems can also be performed in real time.
  • Efficiency: Monotonous tasks can be performed by AI in seconds. Where humans find such tasks vexing, data analysis is a small feat for AI and can be performed more efficiently.
  • Low Cost: Outdated systems for fraud detection took an immense amount of time and yielded a large number of false positives, thus doing more harm than good. Add to that the cost of hiring sufficient human resources. AI tools can help eliminate this dilemma. AI and machine learning tools are provided by SaaS companies that provide them at cost-effective rates.

A post by Amelia Matters (1 Posts)

Amelia Matters is author at LeraBlog. The author's views are entirely his/her own and may not reflect the views and opinions of LeraBlog staff.

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