- Many firms are turning to artificial intelligence (AI) and machine learning to provide automated transaction scoring created for online banking (ML) speed and scale.
- Some banks are evolving their methods for spotting suspicious behavior to match business risk profiles, for instance, by creating or strengthening internal financial intelligence teams dedicated to spotting more intricate and strategic concerns involving illegal funding.
When someone uses dishonest, misleading or illegal means to steal your money or assets or otherwise damage your financial well-being, they are committing financial fraud.
A few examples of financial fraud include investment fraud and identity theft.
In today’s times, the risk of being a victim of fraud or other cybercrimes is ever-present and the situation is only getting worse.
Reporting the instances of financial fraud to the proper authorities promptly thus has become crucial.
With online banking come transactional frauds
As the number of online banking users grow, so will the probability of frauds. During the Covid-19 pandemic, more than a third of retail banking customers (35%) preferred online banking. This is likely to become the new norm.
But as the payment sector speeds up transactions, it should be noted that the fraud detection technology will have even lesser time to react!
As of today, companies are striving hard to increase their capacity to detect & prevent fraud, given the substantial costs associated with rectifying financial misconduct. The long-standing KYC and anti-money laundering systems have helped identify fraud, but contemporary criminals are continually developing new ways to manipulate the system.
The companies that are unable to use the latest tools to keep ahead of the criminal actors and the risks are becoming more frequently targeted.
In the same year that identity fraud reached its highest level since 2013, losses from account takeovers climbed by 72%, totaling USD 16.9 billion in losses in 2019.
The retailers and financial services firms are also dealing, as we speak, with merchant fraud, chargeback fraud and the international payment fraud.
Many firms are turning to artificial intelligence (AI) and machine learning to provide automated transaction scoring created for online banking (ML) speed and scale. However, due to the problem’s needle-in-a-haystack nature, AI and ML will need to be applied more quickly and at a larger scale.
Identifying your client digitally and securely
Following the Know Your Customer (KYC) rules has long been a requirement for banks and financial institutions. These are essential for preventing fraud and preserving client confidence. Nevertheless, many people continue to rely on the knowledge-based authentication (KBA) which warrants the details like Names, Residential Address, Social Security Number and the Security Questions to confirm a person’s identity.
But this information is prone to data breaches and theft since this so-called “static information” is updated infrequently. Since this is the case, banks are using more complex methods of identity verification that combine the updated data with already-collected customer data.
A customer’s digital identification will be more effective if it can be updated quickly.
- Financial organizations can give their customers a digital identity that is dynamically updated and tougher to forge by combining traditional customer information with other data sources.
- A digital identity is made up of several sorts and sources of data. Therefore, the difficulty is keeping everything updated rapidly enough to stay one step ahead of crooks while keeping the customers happy.
Taming the money laundering evil
Financial institutions are required by law to comply with the anti-money laundering (AML) regulations and failure to do so can result in severe penalties. Although US regulators have a reputation for being strict, in 2019, European authorities enforced criminal penalties on money laundering that were more severe than those imposed by the US.
Identifying the ultimate beneficial owner—the individual who truly controls or benefits from a transaction—and their line of business presents a problem for financial institutions, who must also keep an eye on client behavior for indications of any suspicious conduct.
Some banks are evolving their methods for spotting suspicious behavior to match business risk profiles, for instance, by creating or strengthening internal financial intelligence teams dedicated to spotting more intricate and strategic concerns involving illegal funding.
Although customer segmentation and risk assessment are frequently employed to combat AML, they can often go wrong, so financial firms are constantly searching for novel approaches to lower the false positives and negatives.
Regardless of the technology available, a corporation may find it challenging to address the global problem of money laundering on its own. Financial services organizations would profit from increased cooperation to assist such corporations in recognizing and stopping the issue – even as new AML solutions are created.
Tackling fraud while keeping customers happy
Financial institutions must continuously strike a balance between the need to stop fraud & cybercrime and making sure that legitimate clients receive prompt and efficient services. Fighting the various fraud techniques requires the capacity to evaluate data fast and spot the patterns.
As more banking is done online, the problem of financial fraud will only get worse. However, businesses that respond to it effectively now will have an instant economic advantage and be better positioned to create reliable fraud detection systems that are even more effective in the future.
Criminals have become more sophisticated and have learned faster techniques for stealing and creating false identities to commit fraud in today’s fast-paced world. Banks must change with the times and abandon their outdated, inefficient RDBMS systems, which cannot support dynamic digital identities and current AI/ML-based fraud detection.
Due to their effectiveness in processing different data models and spotting suspicious trends, ‘native modules’ have been used by numerous financial services companies.
By giving fraud detection platforms real-time access, they can examine transaction patterns quickly and supplement KYC procedures with new tools for digital identity – which ultimately provides banks with greater leverage.
Many institutions are now thus relying on real-time databases to be more adaptable, quick to act and skilled at combating fraud.