How AI fights against financial criminals and money launderers?

As criminal methodologies are growing more advanced, the fight against money laundering is becoming a huge challenge for all the financial institutions around the world. Therefore, it becomes necessary to put in AML (Anti-Money Laundering) measures. As AML requires to deal with a huge amount of customer data, they are turning to AI and Machine Learning, to help them identify and detect money laundering activities.

AI performs AML tasks faster than a human employee and also, through machine learning it possesses the capability to modify new threats and detect new money laundering methods. It ensures that financial institutions are able to adjust quickly to different regulatory environments.

When transaction data of a customer is incorporated into an AML program, AI and machine learning models analyze the behavior to make predictions and perceptions about that customer in the future.

How are AI and Machine Learning advantageous in fighting financial criminals and money launderers?

Customer Perceptions

AI systems enable the CDD (Customer Due Diligence) and KYC (Know Your Customer) systems, to take place at a faster rate and with greater deepness and reach. The AI-based CDD and KYC processes enable the financial institution to

Efficiently identify and collect data from a greater range of external sources which include watch lists, sanction lists, and create a factual profile of the customer.

Recognize valuable owners of customer entities by using external data faster and more efficiently.

Accumulate and reconcile customer data across internal systems to remove replication and errors and intensify the density of AML measures among customers.

Automatically enhance dubious activity reports with appropriate data from customer risk profiles or data from external sources.

Unstructured Data

There are other important steps beyond creating customer risk profiles. As a part of monitoring transactions, screening PEP, screening sanctions, and monitoring media, the AML process requires identifying and analyze the unstructured data. Every financial institution must make an effort to use the unstructured data to recognize their professional, social and political lives by inspecting a range of external sources which includes public archives, media, social networks, etc. in such circumstances, AI helps the institution to recognize those unstructured data. Once the data is collected and analyzed, AI helps the institution prioritize and categorize information to assist risk management.

Reporting Dubious Activity

AI can assist the reporting of doubtful activity by producing reports and also, by automatically filling them with accurate information. After their submission of reports to the authority, SARs goes through a process of internal reporting. AI technology can make the SAR process easy as algorithms can generate automated reports with accurate data and transmute that data into an accessible, standardized language in order to eliminate bureaucratic friction. Because of standardized language and terminology, AI increases the speed and efficiency of an institution’s AML reporting.

Noise Minimization

The AML system is complex and is a time-consuming procedure therefore it is an advantage to incorporate AI within an AML system which helps in adding speed and efficiency. But one of the major hindrances in the process is the level of noise or false positives which is the result of incomplete or inadequate data or over-sensitivity of AML steps. In such cases, AI systems play an important role by generating a significant transformative effect to the level of noise generated during the AML process. AI assists the institution to produce higher insight into customer’s transaction patterns and enables them to remove wrong and invalid alerts which makes the process costly for the institutions and inconvenient for customers. By minimizing noise, AI and machine learning tools enable AML employees to better prioritize and direct the most required money laundering alerts. By doing so AI more effectively contributes to the fight against financial crime.

Limitations of AI

In order to keep pace with the increasing risk of financial criminals and money launderers and the need to react faster to those new threats, often new AI and machine learning models are prematurely dashed into the market without proper training. This creates a huge skepticism around AI and Machine Learning technologies. Therefore, banks must remember that AI experimentation comes with diminishing returns. They should focus on performing strategic, production-ready AI micro-projects in parallel with human teams to deliver actionable insights and value.

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Two important research breakthroughs—one in the US and the other in UK— leverage advanced artificial intelligence technologies to fight the pandemic

Clinicians, academicians and government entities around the world have leaned on new-age technologies such as artificial intelligence (AI), machine learning (ML) and data science, to track and fight the coronavirus pandemic. While AI has greatly facilitated exchanges of views and information between the scientific community, we take a look at two interesting research breakthroughs that have deployed this niche technology to fight the virus.

A team of scientists at the University of Liverpool, UK, has used AI to work out where the next novel coronavirus could emerge, BBC reports. The researchers used a combination of fundamental biology and machine learning. Their computer algorithm predicted many more potential hosts of new virus strains than have previously been detected.

The scientists say their findings could help to target the surveillance for new diseases – possibly helping prevent the next pandemic before its starts.

Using AI, another research team at the University of Southern California’s Viterbi School of Engineering developed a method to speed up the analysis of vaccines and zero in on the best potential preventive medical therapy. The method is easily adaptable to analyse potential mutations of the virus, ensuring the best possible vaccines are quickly identified —solutions that give humans a big advantage over the evolving contagion. Their machine-learning model can accomplish vaccine design cycles that once took months or years in a matter of seconds and minutes, the study says.

“This AI framework, applied to the specifics of this virus, can provide vaccine candidates within seconds and move them to clinical trials quickly to achieve preventive medical therapies without compromising safety,” said Paul Bogdan, associate professor of electrical and computer engineering at USC Viterbi and corresponding author of the study. “Moreover, this can be adapted to help us stay ahead of the coronavirus as it mutates around the world.”

The AI-assisted method predicted 26 potential vaccines that would work against the coronavirus. From those, the scientists identified the best 11 from which to construct a multi-epitope vaccine, which can attack the spike proteins that the coronavirus uses to bind and penetrate a host cell.

Moreover, the engineers can construct a new multi-epitope vaccine for a new virus in less than a minute and validate its quality within an hour. By contrast, current processes to control the virus require growing the pathogen in the lab, deactivating it and injecting the virus that caused a disease. The process is time-consuming and takes more than one year; meanwhile, the disease spreads.

USC’s AI-assisted method will be especially useful during this stage of the pandemic as the coronavirus begins to mutate in populations around the world. Some scientists are concerned that the mutations may minimise the effectiveness of vaccines which are now being distributed.

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Traditional network security tools have become outdated in the face of sophisticated cyberattacks. Our cybersecurity strategies should embrace latest technologies, such as A.I. and machine learning.

For many years, traditional network security tools such as firewalls, anti-virus software, web proxies etc. have been the go-to defences for organisations. While these tools were effective to a certain extent in the past, the dramatic changes brought to the digital world by “Industry 4.0,” over the last decade, has seen a dynamic shift to the cyber-threat landscape thereby reducing the effectiveness of these traditional tools.

As we continue to embrace digital revolution in all aspects of our life, the threat to the cybersecurity landscape is only increasing with each passing day. Cybercriminals, today, are using cutting-edge technologies to launch destructive cyberattacks on large corporations that have far-reaching consequences as was seen in the case of Adobe and Equifax, and India being at the forefront of digitization has become the prime target for cyber criminals. In fact, as per the Acronis Cyber Readiness Report of 2020, India is reporting more cyberattacks than any other country in the world.

From an organisational perspective, apart from loss of critical information, financial losses, reputational damages and disruption in operations, in most cases, it becomes impossible to identify the intensity of the cyberattack, and the amount of data that was actually compromised often remains unknown. This was witnessed even recently when hackers launched attacks on multiple Indian pharmaceutical companies where, till date, there is no visibility on the degree of attack and the nature of data that was compromised.

Cybersecurity is a critical aspect for all organisations today. Unfortunately, most businesses are not adequately equipped to handle these complex cyber threats simply because they continue to rely on traditional techniques. They do not possess the high-end tools required to quickly identify and recover from threats which, if adopted, can go a long way in ensuring cybersecurity. For instance, a study conducted by Cisco in 2019 revealed that A.I. based tools can identify up to 95% of threats faced by an organisation. That being the case, for a country that thrives on information technology, it is critical that organisations transition from traditional solutions to technologically advanced solutions at the earliest.

While talking about technologically advanced solutions, organisations should start depending more on artificial intelligence (A.I.) based tools. Unlike traditional techniques that neutralize the effect of vulnerabilities only upon the identification of the same, the approach becomes very different with the aid of A.I. and machine learning enabled tools. A.I.-based systems are proactive in detecting vulnerabilities since they can analyse patterns and discover loose ends beforehand thereby enabling organisations to take preventive action before they are even affected with a security incident.

For instance, A.I. techniques like “User and Event Behavioural Analytics” can be used to analyse baseline behaviour of accounts and identify anomalous behaviour that might signal a zero-day cyberattack. This can protect organizations even before vulnerabilities are officially reported. An A.I. vendor named ‘Darktrace’ provides a software that utilises A.I. to understand the behaviour of each user, and the software automatically sends out an alert if there is a vital deviation from the normal baseline behaviour. Additionally, apart from using A.I. enabled solutions, organisations should also adopt simple measures like the use of a multi-factor authentication (M.F.A.) process to secure their systems. MFAs can help prevent some of the most common types of cyberattacks, including phishing, brute force and man-in-the-middle attacks.

It is important to remember that hackers are only becoming sophisticated by the day. It is not sufficient to simply introduce tools that ensure cybersecurity. It is equally important that organisations constantly understand the loopholes in their security systems and take measures to fix the same. For this purpose, organisations such as Tesla, Google etc. are increasingly turning to crowdsourced security measures, such as bug bounty programs, to find loopholes in their security systems, by hiring ethical hackers. In fact, many organisations are substituting their traditional penetration testing efforts with crowdsourced security measures since they offer a plethora of benefits including the ability to identify and fix vulnerabilities faster, paying for valid results rather than effort or time and varied expertise of hackers.

However, these techniques, be it A.I. enabled tools or crowdsourced security measures, can never work in isolation no matter how advanced they are. The effectiveness of the cybersecurity architecture of an organisation ultimately depends on the over-arching security model. This security model, thus, should not focus on tools that are merely reactive in nature. Instead, the overall security model should comprise of tools that prevent, predict, detect, and respond to threats in an efficient manner, and this is where the concept of adaptive security architecture comes to play.

Adaptive security, the buzz word in recent times, is an approach that analyses behaviours to protect against and adapt to threats even before they happen. Adaptive security architecture (ASA) is a concept and there are no pre-defined techniques on what constitutes ASA. Thus, organisations have the flexibility to introduce curated techniques (such as A.I.-based tools) so long as such techniques are able to predict, prevent, detect and respond to threats (elements of ASA) in a timely manner. For example, an implementation of ASA is the Emsisoft anti-malware that monitors the behaviour of all active programmes and sends out an alert if suspicious behaviour is detected. As opposed to focusing on preventive measures, ASA is built on the foundation of a more responsive, receptive and real-time outlook when protecting an organisation’s security systems.

While organisations can enforce technologically advanced protocols for ensuring cybersecurity, the role of personnel can never be ignored. Human error has a well-documented history of causing data breaches. This was seen when Equifax’s system was compromised for two whole months simply because of an oversight by the IT team. According to the UK Information Commissioner’s Office, human error was the cause of approximately 90% of data breaches in 2019. This only implies that cybersecurity is a top-down approach. Every single employee, from the CEO to the supervisor, plays an important role. That being the case, it is important that employees understand what they can do to protect the company’s digital assets, how to avoid falling for cybersecurity attacks, and who they should report potential incidents to.

On a concluding note, as India moves towards a five trillion dollar economy and with the IT sector leapfrogging through multiple stages of development faster than many western economies, there is an imminent need for organisations to invest in advanced technologies and personnel training to ensure a watertight cybersecurity architecture.

Views are personal. Bhushan is Partner and Chennai head, Shardul Amarchand Mangaldas & Co and Viswanat is Associate, Shardul Amarchand Mangaldas & Co.

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