Mitigating Fraud With Data

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Although technology has improved client convenience for banking, it has also created additional opportunities for fraud. Scams, insurance fraud, credit card fraud, account fraud, and other fraudulent behaviors cost businesses and individuals millions of dollars each year. To reduce risk for institutions, financial fraud detection is crucial. Individual accounts can be quickly drained by con artists, and credit cards can swiftly accumulate $10,000 in charges. Even worse, organized crime groups are capable of pulling off complex scams and stealing millions of dollars. Therefore, it is imperative that companies take action to reduce fraud.

To find out more about what businesses can do to fight against fraud, DigitalCFO Asia spoke with Keith Budge,Executive Vice President of Teradata, Asia Pacific and Japanto gain more insights. 

Financial institutions’ top concern remains fraud. The rise in fraud cases emphasizes the importance of concentrating on mitigation and preventive processes and technologies, as well as the priority emphasized by regulators. However, the practices of the FIs continue to have gaps that require improvement. It is obvious that fraudsters are focusing on the increasing use of digital assets and banking, particularly in the insurance industry. In APAC, there has been an increasing frequency of identified fraud cases, and prospects for fraud risk management are growing as a result.

The increasing dominance of automation and digitisation highlights the growth of financial institutions. With digital transformation, businesses in APAC are embracing the digitisation of business processes by adopting a more positive attitude towards the cloud, data analytics and data management so that they can reduce friction, enhance cybersecurity and at the same time, improve customer experience. Keith Budgebelieves that a business’ ability to protect against fraud lies in the availability of very good data in real-time. The data will come from multiple sources and businesses must have the ability to bring that data to create informed decisions. 

“It is critical in today’s landscape that businesses can serve consumers in the way they want to be served while maintaining compliance with regulatory requirements and making fraud protection a priority,” says Keith Budge, Executive Vice President of Teradata, Asia Pacific and Japan.

Reactive and preventative strategies are neither good nor harmful in and of themselves for any given situation. It is sometimes preferable to take the lead right away, while other times it is preferable to wait and see. But when it comes to fraud, there is no debate over which is preferable because prevention always outperforms detection. 

When it comes to fraud prevention, businesses can identify patterns of behaviour especially those that are unique or new, such as a customer making a large international bank transfer for the first time – the bank will be alerted of this and ensure that the consumer is making such a transaction informatively. With so many online scams ongoing with fraudsters asking for money or donations by faking illnesses etc., banks need to better equip themselves to stop their consumers from falling for such scams. 

Without data, suspicious large transactions or deliberate fraud can be identified. The provider can freeze the account, for instance, if a customer uses their credit card at one retailer and then seems to use it at another store across the country an hour later.

Data, however, enables highly accurate detection of other, more covert indications of fraud. Before a client even realizes that their card or account has been stolen, fraud can be detected by using customer information to predict general trends and identify suspicious transactions. Less transactions are detected as fraudulent thanks to advancements in technology and machine learning techniques.

“Data goes hand-in-hand with machine learning and they can help businesses to recognize previously-learned patterns and search for them in datasets. With this, businesses are able to handle more complex consumer trends that cannot be easily detected by people and have better fraud detection systems in place,” says Keith Budge,Executive Vice President of Teradata, Asia Pacific and Japan.

Data may be shaped to detect and uncover connections between large abnormal behaviors rather than just concentrating on a few outlier accounts or activities. As fraudsters become more proficient, having the ability to automatically detect this fraud will help save financial institutions from suffering huge losses. Financial institutions must be able to identify compromised accounts and lock them down in order to fight crime in real-time without endangering trustworthy customers.

Continuous evaluation and refinement of machine learning techniques, together with better data practices, are necessary to reduce false positives. Data analytics investments in security fraud detection are a crucial part of risk management overall and security best practices. Financial organizations can reduce losses and increase revenues by devoting time and money to big data financial fraud prevention.

Insider threats can be conducted by employees, contractors, and vendors. Insider threats can be done maliciously, deliberately or accidentally. These threats can compromise the company’s customer data and financial data resulting in fraud. With the technologies that are available today, they are able to keep a very close eye on activities and actions that can potentially cause harm to the company. 

“Just like how these technologies can watch the activities carried out by consumers, they can also keep an eye on what is happening within the company – the activities carried out by the employees,” says  Keith Budge,Executive Vice President of Teradata, Asia Pacific and Japan.

These technologies can flag to supervisors and business leaders about suspicious activities that are happening and who is conducting them. But apart from reliance on technologies and data, there are other things that companies can do to further prevent insider threats from occurring. 

Financial institutions must be able to recognize and lock down compromised accounts in order to combat criminality in real-time without harming legitimate consumers. Continuous evaluation and updating of machine learning procedures, including advancements in data mining techniques, are necessary to minimize false positives. Data analytics investments in security fraud detection are a crucial part of risk management overall and security best practices. Financial organizations can reduce losses and increase revenues by devoting time and money to using data for financial fraud prevention.