13.06.2022
A number of banks have breached regulatory compliance in the past and are now on the radar of financial regulators. Fines, reputational damage and career brakes are just some of the consequences.
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Banks and regulators fighting financial crime face an uphill battle – a situation COVID-19 has compounded. The pandemic has brought with it soaring levels of fraud, money laundering and terrorist financing as cybercriminals seek to exploit heightened security risks. And throughout, banks have also had to face the operational challenges of limited services, staff shortages and suspension of conventional servicing channels like branches.
Regulators insisting on ever-greater transparency means financial institutions have to generate and manage increasingly vast amounts of data. And compiling, analysing and computing it all is only feasible with various technology systems and applications. Even so, much of the whole process remains manual, the cost of which becomes excessive.
As regulatory demands and cost pressures intensify, regulatory compliance obligations are straining banks to the limit. The upshot is significant compliance risk and the looming threat of regulatory fines – and having to divert more and more resources to administrative tasks as opposed to risk management.
More than a decade after the global regulatory reforms defined by the 2008 financial crisis, deep-seated changes have forced banks to completely rethink regulatory compliance. A ceaseless flood of new laws and regulations have left them little choice. But understandable though such changes are, the sheer volume has created reams of red tape, complicated and hampered the work of risk and compliance officers and forced financial service providers to focus on regulatory compliance rather than innovation.
The answer lies in digitalization. Financial firms need a sustainable, scalable and cost-effective solution to analyse enormous amounts of data more efficiently, identify suspicious activity more easily, and improve and accelerate risk detection and remediation.
Regulatory Compliance software uses cutting-edge technology like machine learning to help financial firms solve regulatory and compliance issues swiftly and at scale. Combining data with the expert insights of compliance officers helps regulatory compliance software detect complex cases of money laundering and terrorist financing faster and more effectively than ever before.
In recent years, demand for regulatory compliance software has skyrocketed as organisations struggle to cope with increasing regulatory pressures. Reflecting this, the global RegTech market is expected to grow from USD 6.3 billion in 2020 to USD 22.2 billion by 2027, at an impressive Compound Annual Growth Rate (CAGR) of 20.1 percent over the forecast period, according to a recent analysis from Research and Markets.
Given increasingly complex threats and an avalanche of data to analyse, current compliance processes, largely tied to manual, repetitive and data-intensive tasks, are no longer fit for purpose and fall short when it comes to detecting risk and fraudulent activity.
Fortunately, financial firms now have scope to fight back and leverage AI-powered, tailored and automated compliance and risk management systems. New regulatory compliance software allows banks to analyse huge volumes of data from numerous sources at exceptional speed – automatically flagging security risks or suspected illegality. Combining data with expert insights from compliance officers helps machine learning RegTech software detect complex cases of money laundering and terrorist financing swiftly and accurately.
Regulatory compliance software slashes the time required for complex analyses and checks. In fact, it’s a game-changer – allowing huge productivity and efficiency gains, cutting costs and delivering better and more accurate data results. The long-sought after solution to scale and adapt to the modern threat and regulatory landscapes is now a reality.
With surging criminal activities, shortages of skilled workers, cost pressures and new laws and regulations, the pressure on compliance departments of banks and insurance companies has never been greater. But whatever happens, companies are desperate to avoid reputational risk or coming onto the radar of financial regulators. One solution is to use regulatory compliance software that relies on machine learning as an AI component.
Its advantages include scope to analyse complex data in bulk efficiently, detect risks early and boost agility. Intelligent digitalisation paves the way to implement complex regulatory requirements as well as helping mitigate risks and keep costs under control. All of which means key personnel can focus on the essentials. When selecting the right regulatory compliance software and systems supplier, banks should ensure they have in-depth knowledge of regulatory compliance in banks. Everything points to this future-proofing approach as the best way to handle fresh challenges going forward.
To learn more about how digitalization and machine learning aid compliance, download the whitepaper.
As well as an overview of the latest SAR figures, new regulations and key steps towards automation in compliance, the paper also includes 9 lessons learned that should be considered when implementing machine learning..
Be quicker than criminals, reduce the cost of false positives: Machine Learning helps banks determine which clients and transactions pose a compliance risk.
Show meThere are 5 reasons why banks need explainable machine learning models. Download the checklist and learn what is important.
Download ChecklistIn this interview, Thomas Ohlemacher from Actico explains how machine learning can assist compliance management and how banks can increase the traceability of decisions made using this technology.
Read the Interview
