Technology / #ForbesTechnology

April 4, 2018,   8:30 AM

Can RegTech Help The Financial Industry Fight Crime?

Syeda Mehar

Financial Crime Risk Management, Regulatory Compliance and Technology Consultant and Auditor FULL BIO

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Over the past decade there have been rapid global developments in the field of compliance, with a focus on fighting financial crime. To continue raising the bar in the tech era, financial companies could be using RegTech to boost their anti-money laundering (AML) regimes and aide the combating of financial terrorism (CFT).

Current global AML/CFT structures are suffering from two major weaknesses that are affecting the implementation of compliance laws and regulations.

First, both financial and law enforcement institutions have experienced wide-spread resistance when it comes to sharing information, especially across borders. This opens up the possibility of an exploitation of supervisory gaps. Major hindrances include data privacy laws, poor data quality and the absence of data structures and standards, which reduce quality analysis.

Second, there is continued ambiguity in international regulations, which is causing segmentation among jurisdictions and creating complex sets of conflicting requirements. Disagreements over primary offences of financial crime still exists among authorities and there is uncertainty among financial institutions regarding know your customer (KYC) obligations, which leads to instances of non-compliance.

Financial institutions are the gatekeepers to fighting financial crime. The identification of suspicious activity and the reporting of it to intelligence units are major responsibilities for the financial industry. Institutions fulfil their role by performing two actions. First is the implement of KYC and customer due diligence (CDD) processes to gather information on clients’ activities and sources of wealth in order to build a risk profile status. Second is the ongoing monitoring of client accounts and transactional history to detect illegal and suspicious areas of concern.

Key Benefits of Regulatory Technology (RegTech)

RegTech is a sub-set of FinTech that focuses on technologies to facilitate the delivery of regulatory requirements more efficiently and effectively than existing capabilities, and it has been gathering fast momentum over the last year across the regulatory compliance market.

RegTech has the potential to enhance the ability, speed and efficiency of financial institutions to analyze and share data to detect financial crime and report it according to regulatory requirements. By implementing transformative technologies, the financial industry can:

    • more effectively detect suspicious activity and frau


    • reduce incidences of human error


    • enhance secured communication between financial institutions and clients


    • reduce the cost of compliance


    • increase the scope of financial inclusion

Technology can enable faster and better data sharing, and the atomization of systems can reduce barriers to financial systems while maintaining a robust risk-management environment. As compliance evolves, the following technologies can build smarter platforms to meet regulatory obligations.

Big Data

Big Data has been a commonly whispered term used for an explosion in volume, variety and speed of information gathered for analytics purpose. Big Data is composed of the following:

    • Traditional enterprise data: for example, customer information systems, ERP data, online transactions, financial data (general ledger, accounts payable, accounts receivable).


    • Machine or senor generated data: for example, Call Details Records (CDR), weblogs, smart meters, manufacturing sensors, equipment log and trading system data.


    • Social data: for example, customer feedback streams, blogging sites and social media platforms.

Big Data and analytics are shaping how financial institutions deter sophisticated crime. They allow organizations to integrate technology platforms, methodologies and analytics to identify a holistic and real-time view of suspicious activity.

Machine learning

Machine learning enables machines to detect patterns and make decisions. It recognizes patterns in order to discard false positives and focus on genuine risks. It further digs down to flag up false negatives, which are overlooked by legacy systems and represent significant risks to financial institutions.

These applications can analyze large amounts of transactional and client information from a variety of data sources, such as transaction monitoring systems (TMS), KYC systems, Lines of Business (LOB) customer information, investigative databases, public internet sources and the deep/dark web, where criminals often interact and transact business.


Biometrics is the automated recognition of individuals based on their biological traits, e.g. fingerprints, face, iris or palm print. Sensor images are analyzed by applying deep learning algorithms and comparing stored information in databases to establish identity of individuals.

Financial institutions ascertain a client’s identity during the on-boarding process and use it throughout their interaction with the client. There are two major issues in applying biometric systems. First are the different levels of accuracy in biometric technologies—for example, finger print scanning is less accurate than iris scanning. Second is the security of biometric information.

The performance of biometric systems is affected by:

    • Imposter attacks: Impostors will attempt to be recognized by exploiting a biometric system’s limitations.


    • Spoof attacks: Spoofing is the practice of fooling a biometric security system using fake or copied information.


    • Biometric stored data: Stealing biometric data can be disastrous, resulting in the permanent loss of biometric identities when criminals duplicate biometric patterns.

Securing interactions between financial institutions and their customers is a constant battle with hackers. Multi-factor identity authorization, encryption technologies and cyber security locks are being developed and applied to secure data transmission and communication.

Robotics and Artificial Intelligence (AI)

Robotics and AI automate activities that normally require humans to use intelligence and make decisions. Robotic Process Automation (RPA) has been used to improve the overall efficiency and quality of financial crime risk management and deliver a resilient round-the-clock operation. RPA “bots” can be validate customer records and expedite approvals, or document business relationships such as commencement dates or customer ID data. Robotics and AI can also help financial institutions reduce handling times and embed simpler, shorter processes.

Firms are using external data sources, such as news feeds and social media, in conjunction with client and transactional data to leverage AI technology with greater accuracy. It can also be future-proofed to address tomorrow’s challenges and technologies.

Shared Systems And Distributed Ledger Technology (DLT)

DLT provides a single source of truth by requiring that any change in a database be verified. It can also serve as a safe repository for unique identifiers for transactions, legal entities and clients.

Financial institutions are already using KYC repositories in which various institutions can store and share CDD information on a centralized basis. Limitations to this include different standards of data, liability issues, gaps in data and the fact that no system contains all the relevant information.


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