When companies capitalize on the potential of data science, they are creating value and more avenues of growth. With the data science revolution, industries have been flooded with data information that contributes to the expansion of financial functions, promotes efficient operations, and provides a multitude of professional opportunities for finance and accounting professionals. Financial institutions that adopt new technologies to meet regulatory compliance regulations and innovate in the global banking environment will be the new industry leaders. Data analytics can help companies meet financial compliance regulations by improving transactional monitoring, sanctions screening, and conforming with Know Your Customer regulations.
Generally speaking, data science—a set of methods and operations used to process data and extract insights that add value to a company—has become an integral part of the success of any high-functioning company. Data science includes data interpretation, understanding methods to move and apply data, using data to inform essential business needs, and gleaning valuable insights as companies pivot forward in their endeavors.
A multitude of factors motivate the need for the financial sector to collect, store, and analyze significant volumes of information: the modernization and commodification of financial products and services, increased volume of activity, the need to improve risk management, and increased regulatory compliance.
As a result of more significant regulatory compliance laws, the finance industry has amassed greater volumes of data. To adequately sift through the masses of information, certain data techniques can be used to automate and accelerate much of this data interpretation to reduce operational costs and leave data scientists to focus on action steps. Such processes include machine learning, artificial intelligence, natural language processing, and cognitive automation.
In addition to lightening the data interpretation workload, compliance teams leverage data to manage and prevent financial crimes with proactive risk management and by monitoring transactional activity, enhancing screening activity, and informing Know Your Customer procedures.
Monitoring transactional activity with anti-money laundering regulations
Anti-money laundering (AML) refers to regulations and procedures used to prevent criminals from disguising illegally obtained funds as legitimate income. The use of machine learning models will enrich transaction monitoring alerts and increase Suspicious Matter Report (SMR) conversion rates to predict AML situations before they occur. Enrichment also provides vital details about customers, accounts, or beneficiaries associated with the alerts. Such details may include prior cases, SMRS, and Transaction Threshold Reports (TTR), existing scoring processes that assess the risk of a transaction or customer account, or external information regarding law enforcement inquiries, subpoenas, etc.
Machine learning models, when compared to traditional models, have been demonstrated to interpret data more efficiently, provide greater accuracy, and are better at predicting significant events.
Enhancing Sanctions Screening
With rapidly demanding changes in the financial sector and increasing regulatory demands, the capabilities of risk detection programs and screening engines are struggling to keep up with what companies need. Poor screening can resemble a large backlog of screening alerts and alarmingly high levels of false positives, which can directly impact operational costs and slow a company’s successful output.
An effective screening solution requires a screening engine that provides a thorough investigative process and accurate data delivery. Data must be complete, high quality, and result in actual positive detection rates and operational efficiency. In addition, machine learning techniques must be paired with accurate predictive calculations. Therefore, data scientists helping to build and perfect screening processes are more likely to contribute complete and detailed data and optimize processes that will diminish false positive rates and provide more precise screening.
When companies focus on enhancing the sanctions screening processes, they will benefit from operational efficiency by reducing the effort and cost of data interpretation and thorough screening processes.
Know Your Customer Procedures
Data can also better protect the relationship between financial institutions and customers. Know Your Customer (KYC) procedures in the securities industry are designed to protect financial institutions against fraud, corruption, money laundering, and financial terrorism. They are critical in assessing customer risk and a legal requirement to comply with Anti-Money Laundering (AML) laws. KYC standards require financial institutions to establish and verify customer identity, understand the nature of customers’ financial activities, and assess customers’ money laundering risks. When financial firms fail to comply, heavy penalties can be applied.
Data science is critical in informing KYC insight, and the more data financial institutions can use to access and evaluate customer records, the better they can protect customers. Financial firms will be better able to trace customer records, identify duplicate accounts, and update records to ensure customer integrity and financial compliance requirements.
KYC compliance and AML are becoming more prevalent as more financial institutions dedicate significant time and resources to KYC compliance processes. Protecting both clients and investment advisors, KYC compliance supports the safer transfer of money while providing greater levels of transparency, trust, and risk mitigation.
Data science interpretation and informed processes will benefit companies by better informing their financial compliance regulations imposed by different laws and authorities. By understanding how data science supports companies in meeting financial compliance regulations through improving transactional monitoring, sanctions screening, and conforming with Know Your Customer regulations, companies can better verify the identity of their customers, increase the accuracy of the information, protect the relationship between financial institutions and customers, improve risk management practices, and more effectively mitigate the consequences of financial crime. With these streamlining and improved processes, companies can be left to focus on driving the success of their business and the longevity of their legacies.
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