Artificial intelligence and machine learning are helping companies to optimize internal processes to improve efficiency, understand the sense of large amounts of data to facilitate smart decision-making and create new and innovative services to improve customer experience.

One area where artificial intelligence and machine learning are having a special impact is financial services, particularly in the area of combating fraud, which are expanding.

For example, the takeover account, which consists of taking account of the credentials of access to an account to use it illegally without the legitimate user’s knowledge, grew significantly in 2018, with a number of attacks tripled compared to the

How do artificial intelligence technologies and machine learning help to combat these frauds? We asked Tim Bedard, Director of Security Product Marketing, OneSpan, an expert on the subject, who identified five modes.

Accuracy of data analysis

One of the most important features of machine learning algorithms is that they are able to analyze large amounts of transaction data and report in real time those suspicious by assigning highly accurate risk scores.

This risk-based analytical approach detects complex models that are difficult to identify for analysts, which means that banks and financial organisations become much more operationally efficient and at the same time manage to detect more fraud.

Algorithms take into account several factors, including the customer’s location, device used and other contextual data, to create a detailed picture of each transaction. This approach improves decisions in real time and protects customers more effectively from fraud, all without affecting user experience.

And this trend will continue in the coming years. Thanks to significant technological development in this area, organizations will increasingly rely on machine learning algorithms to decide which transactions are suspicious.

Optimization of the work of fraud analysts

The acceleration of new cyber threats, combined with a large amount of data to be examined, requires fraud analysts to be almost impossible to identify any suspicious element in a timely manner. Therefore, financial institutions must adopt an innovative approach that allows rapid analysis and extraction of data across multiple channels and, at the same time, detection of fraud in real time.

With artificial intelligence, data analysis is completed in milliseconds, efficiently identifying complex patterns that can be difficult to identify for a human analyst.

This reduces the amount of manual work needed to monitor all transactions, because only a smaller number of cases require human attention.

The quality and efficiency of the work of fraud analysts also increases, as they are freed from time-consuming tasks and can only focus on the most important cases, for example those where risk scores are highest. This reduces the cost of anti-fraud activities and increases the rate of genuine transactions successfully processed through a better risk assessment.

Reduction of false positives

With the level of complexity achieved by the current financial infrastructure, the positive false expression has become closely associated with the industry’s attempts to combat fraud. As a result, one of the biggest challenges in the banking sector is to minimise the amount of false positives generated, thus saving time and money and avoiding frustrating customers unnecessarily.

Artificial intelligence and machine learning play an important role in this area, as they are able to analyse a much wider range of data, connections and fraud patterns, including those not yet known to analysts, the prevalence of false positives may be

This results in fewer rejected customers for reasons of fraud and the reduction in labour costs and time associated with the allocation of personnel dedicated to the revision of reported transactions.

Effective detection of attacks

As mentioned above, machine learning algorithms are capable of identifying fraud patterns in large groups of structured and unstructured data. This makes them significantly better than human control in detecting new attack techniques.

Whether it is the ability to predict peak traffic from unusual sources or to create detailed customer profiles to detect anomalies before they develop, the most effective detection of attacks is one of the main advantages offered by artificial intelligence and machine learning. And as these instruments become more powerful, the prospects for banks and financial institutions improve exponentially.

Compliance with the regulations

A system of prevention of fraud based on manually defined rules and policies can no longer keep pace in today’s digital banking ecosystem. To remain at the forefront, financial institutions therefore need a fraud detection solution that exploits artificial intelligence through supervised and unsupervised machine learning.

Machine learning allows organizations to analyze contextual data through devices, applications and transactions and requires very few manual inputs. This means that policies can be constantly adapted, which is essential to maintain regulatory compliance over time (e.g. PSD2). This can save banks time and minimise the risk of costly fines.

Finally, it is important to remember that these different elements cannot be considered in isolation. They are all fundamental pieces in the overall fraud prevention puzzle, which unite to help the banking sector protect customers and to combat the hugely expensive problem of financial fraud.

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