The international banking community is working together to fight money laundering, terrorist financing and cyber fraud. And as cross-border payments become instant, financial crime compliance will become even more difficult.
"To ensure no financial or reputational consequences, institutions must make better use of payment data and analytics to gain greater transparency on transactions.”
At the same time, payment data volumes are exponentially growing. But these data are lacking standardisation and are often spread across multiple IT systems and subsidiaries, being delivered in several formats. To mitigate these challenges, the need to harness payment data has become imperative.
Financial crime has increasingly become a concern for consumers, businesses and governments globally. The impact of financial crime varies in different contexts and it is widely recognised the prevalence of economically motivated crime is a substantial threat to the development and stability of economies.
Fraud is one of the most common types of financial crime. It occurs when individuals or businesses deprive victim of money or harm their financial health through misleading, deceptive or illegal practices.
This can be done through a variety of methods such as identity theft to make unauthorised transactions or investment fraud. Money laundering is also a notable financial crime, where criminal profits are disguised and processed with legitimate earnings. Dangerously, this can lead to terrorist financing and fundings of illegal malicious institutions.
In such an environment the need for access to accurate insights into payment data remains key to compliance and to help avoid financial loss and reputational damage.
Leveraging payment data
Financial crime compliance comes with a variety of challenging factors, such as system complexity, legacy infrastructure, the lack of standards and the possibility of losing data throughout the payments chain.
Financial institutions can take the following steps to address these challenges and mitigate risk:
- Get the big picture on payment activities: complete view of all message data for payments and trade-related messages across branches and correspondents is vital to identifying and quantifying global risk within institutions transaction activities
- Monitor for irregular activity: Financial institutions typically know who they receive payments from but can struggle to identify the other banks sitting behind the transaction. The ability to monitor payments traffic across networks can mean that organisations can proactively find potential risks and ensure their risk policies are being followed
- Enhance payment data quality by identifying flaws and blind spots: With the increased regulatory focus on originator and beneficiary information, payments data quality needs to be continually assessed. This will help institutions identify problems and take action to ensure payments meet relevant standards. Evidence from an independent source can enable financial institutions to discuss the subject with counterparties
- Expedite decision making with the latest analytics: While assembling strong analytics program to deliver insights can be costly and time-consuming for financial institutions, they should consider the return on investment of building the tools in-house versus purpose-built analytics tools that configure to different risk appetites and reporting requirements
- Welcome a community approach to tackling challenges: Compliance teams are continuously challenged by unexpected costs and lower efficiency. As the regulatory landscape evolves it is critical to use the right data and technologies to make risk-based decisions. Working with financial cooperative societies and communities like Swift can help evolve analytics and better address industry challenges
As Swift is embedded in payments, it is dedicated to helping banks use data to support financial crime compliance. Financial institutions can use Swift’s Compliance Analytics to comply with global regulatory standards. Banks and financial institutions will be able to better identify, analyse and address compliance and fraud risk.
Furthermore, there is potential in the future for artificial intelligence (AI) to assist. One of Swift’s early objectives with AI is to develop stronger anomaly detection by enhancing its Payment Controls and Payment Pre-validation.
Using machine learning, Swift can enable more accurate analysis and fewer false positives and rejected messages. This means global and domestic transactions become frictionless while still complying with financial crime compliance regulation.
Unfortunately, financial crime and compliance is not a phase in the banking industry and will continue to disrupt and challenges organisations into the future. To ensure no financial or reputational consequences, institutions must make better use of payment data and analytics to gain greater transparency on transactions.
Promisingly, this can be achieved if the financial community works together.
Suresh Rajalingam is Head of Oceania at Swift.