Asia’s finance industry is one of the most active and lucrative in the world. However, this profitability has seized the attention of cybercriminals, Wee Tee Lim of Cloudera writes in an article on finews.asia.
A recent report named finance the most attacked vertical in Asia. The scope of these attacks extends far beyond enterprises. Singapore saw a surge in scams by 64.5 percent in the first half of 2023 – mostly attributed to criminal activities on e-commerce and online channels.
The complexity of fraud is also rising. Recently, scammers produced a deepfake video advertisement of a Singaporean newscaster interviewing Elon Musk, where their images and voices were manipulated to direct users to invest in a dubious project.
This incident is not isolated; experts from Deloitte have warned that fraudsters will use generative artificial intelligence (AI) to enhance attacks. Why is this happening?
Every New Digital And Finance Channel Adds New Fraud Opportunities
Rapid digital transformation in Asia has ushered in a multitude of online services, enhancing consumer convenience. For instance, some countries now allow citizens to sign up for banking services with their national digital identity and use financial aggregators to manage portfolios centrally.
However, this interconnectedness provides cybercriminals with numerous entry points. Gaining access to just one service can expose a wealth of personal data. Easily accessible generative AI tools have also made social engineering attacks, like phishing, even more convincing.
Resource-Intensive For Many in the Private Sector
While the public sector is moving quickly to address fraud with regulations like shared responsibility frameworks, fighting AI-driven fraud is increasingly resource-intensive for many in the private sector. Investigators are spending more time diving into countless records and behavioral indicators to identify fraud. And just as one fraud trend is countered, criminals employ a different attack method.
How can financial institutions better fight fraud in the face of high-cost pressures – while adhering to tightening compliance regulations?
Mobilizing AI And ML to Fight Fraud Begins With Trusted Data, Trusted AI
Fortunately, AI can also be mobilized for good. AI and machine learning (ML) tools can help organizations to spot signs and patterns of fraud in real time. These tools also help fraud investigators automate behavioral analysis and decision-making - reducing costs and enhancing productivity. When implemented organization-wide, these technologies can significantly bolster predictive fraud defenses.
Mobilizing AI and ML to fight fraud is most effective when organizations can pull together sources of trusted, real-time data to train the models to spot behavioral trends. Training datasets must be as complete and relevant as possible – for example, businesses should integrate data that provides behavioral insights, like banking records and credit scores, so that AI can recognize indicators of fraud well.
Dealing With AI Hallucinations
Organizations must also ensure they have the proper infrastructure to support AI development, as unifying, cleaning, and preparing this sea of data for training takes an enormous amount of computing power.
Without quality data to train AI and ML tools, investigators could end up dealing with AI hallucinations and more false positives, which wastes time and weakens the trust that business leaders might have in such solutions.
How Financial Organizations Are Combating Fraud Risk With AI And ML
Many financial institutions are overcoming these challenges by investing in newer data management technologies, like hybrid data platforms, to facilitate the integration, governance, and analysis of data in real-time across environments securely and compliantly. Critical to this success is an inbuilt layer of security and governance that can be applied consistently.
UOB, a large bank in Singapore, transformed its people, processes and technology as part of its holistic efforts to combat fraud. The bank built its first data mart following the creation of its enterprise-wide data platform, with the data mart currently processing data from more than 40 source systems, including customer information, accounts, and financial and product dimensions. By developing new machine learning models on the platform to enhance fraud analysis, UOB has reduced false positives of suspected money laundering transactions by 40 percent.
Next Wave of Transformation
Aiming to build on the success of the data mart, the bank intends to roll out a new transaction-level data mart designed to support its anti-money laundering efforts and drive more data analytics capabilities throughout the organization. Through its data and this platform, UOB is confident it can leverage data further to trigger the next wave of transformation for its people, processes and technology.
Similarly, Axis Bank, the third-largest private-sector bank in India, has also turned to AI and ML. By using an organization-wide data management platform, Axis Bank can analyze 750 terabytes of data from several sources in Axis Bank’s systems for credit and marketing analytics in addition to fraud detection.
New Data Technologies to Mobilize AI And ML Against Fraud
To effectively combat fraud, organizations must design comprehensive strategies and policies to enable staff to harness new tools effectively. Here are four steps organizations can take:
- Organize data into a single golden source of truth
AI models must be trained on data that is relevant and complete. Organizations should build data pipelines using open standards and interoperable data formats to ensure that data can be collected, integrated, processed, and moved freely from sources across the enterprise to training datasets. - Enhance data governance practices to ensure data used by AI is trusted
Data collected across the organization must be clean for AI models to deliver accurate insights on fraud. Establishing a data stewardship working group to train employees on data governance, conduct regular audits, promote best practices, and enforce compliance will ensure that the data used is trusted and AI-ready. - Enable real-time data use to enable AI to pivot to evolving threats
AI must harness data in real-time to predict threats. Companies should look to implement technologies that accelerate time-to-insight, for example, streaming analytics solutions that enable data teams to analyze data that’s moving from source to destination. - Use data management platforms to better coordinate the fight against fraud
Managing numerous streams of data across environments, training multiple AI and ML models, and enforcing data governance enterprise-wide is not easy. Organizations should use data management platforms to enable stakeholders to simplify, centralize, and improve command and control. Businesses should also use platforms that enable stakeholders to foresee and manage compliance risks.
Financial Institutions Need Predictive, Real-Time Data to Stay Ahead of Fraud
Preventing fraud is a game of cat-and-mouse. When the next digital channel becomes popular, financial criminals will be there, finding completely new ways of committing fraud.
While criminal tactics and channels may change, one thing remains constant: predictive, real-time data, AI and ML will be key to helping financial institutions stay ahead.
Wee Tee Lim is the Regional VP for ASEAN & Taiwan at Cloudera, a U.S.-based software company providing an enterprise data management and analytics platform.
