The fight against financial crime is invariably getting harder. The fintech industry has exploded in recent years, leading to an increase in both access channels and digital transaction volumes. This has made monitoring transactions for potential fraud increasingly challenging.
The fight against financial fraud and other types of financial crime poses one of the biggest challenges for modern global financial organizations. Criminals are taking advantage of the ever-changing and far-reaching financial technology landscape to employ attacks on a massive scale, exploiting multi-channel vulnerabilities. It is becoming more and more important for organizations to apply a diverse and robust set of counteractive measures to protect against capital loss and customer harm. By utilizing automation and data analytics, financial organizations can do a better job, faster, for less cost.
The Power of Machines
With the enormous amount of information flowing in and out of banks at any given time, it would be impossible for human surveillance to keep up.
Except the way that banking is currently done, that’s exactly what has to happen.
The traditional method of regulatory reporting via human writing is still common practice in banking today. Cases of potential financial fraud are physically reviewed by several levels of investigators before they report them to regulators. Because of this, risk alert backlogs are often growing at a faster rate than analysis teams can go through them, leading to greater-than-necessary process times in everyday transactions.
Various machine data and analytics techniques such as artificial intelligence, natural language processing, machine learning, and cognitive automation can be used to speed up processing times, and often leads to a greater level of accuracy. Anti-fraud teams are also utilizing advanced analytics to enhance sanctions screening performance, monitor transactional activity in real time, and improve the KYC process.
In the future, implementing tech driven analytics strategies into a risk strategy could present the following benefits.
1. Learning From the Past
Data collected during the KYC/AML processes can be inputted into a machine for further analytics, and for artificial intelligence training and machine learning. This could help machines learn to better recognize potential fraud earlier and with less human involvement.
2. More Efficient Transaction Management
By utilizing AI and machine learning algorithms, banks can account for multiple sources of information about the customer and their transactions. This can help combat false positives, and identify real suspicious activity that might not have otherwise been detected.
3. Data Gathering Solutions
Use of automated client identity verification via biometrics and artificial intelligence capabilities has become a market standard. But regulatory compliance requires that a huge volume of data needs to be extracted, standardized, and verified. By utilizing technology, organizations can standardize to allow for easier access to financial records and risk profiles while still complying with regulatory measures.
4. Automated Screening
The effective use of machine learning and artificial intelligence in things like PEP and adverse media screening would help alleviate pressures from analysis teams, and lessen the likelihood of error. These may also spur a more thorough data collection process, through the use of expanded sources.
5. Predicting Risk
The use of technology in analyzing and recognising patterns, and subsequently making predictions, could demonstrate tremendous upside in monitoring potential threats.
Making Solutions Easier
The use of financial services is growing rapidly, as is financial fraud and exploitation. It is in banks’ best interest to ensure proper security measures are in place.
There has recently been an increased aptitude among banks to do more than simply flag suspicious activity for compliance and instead utilize data-driven patterns to prevent criminal behavior before it happens in the first place. This would reduce screening pressures, in addition to lessening the likelihood of anything slipping through the cracks.
Dedicated artificial intelligence development, in association with advanced analytics and pattern recognition, has the potential to tremendously enhance the efficiency of the investigative process. Properly utilizing technology for counter-fraud measures is imperative to banks tackling financial crime.
Part of a Bigger Strategy
Financial fraud execution is becoming increasingly sophisticated and daring; those who practice it are pushing the limits, oftentimes utilizing technology to gain an advantage. As such, banks need to design and implement a multifaceted risk management strategy in order to keep up.Data-analytics and artificial intelligence systems developed for this purpose should be designed for easy integration into existing strategies, and should not exist as sole risk management resources. The key lies in using this technology to augment human processing, rather than seeking to replace it entirely.