Data Products: Transforming Fraud Detection in Financial Services

By Suki Dhuphar, Head of EMEA, Tamr.

Cybercrime assumes many shapes and forms. As a result, it’s often challenging to identify fraudulent behaviour and subsequently address it. Traditional methods frequently fail to detect and combat illicit activities, leading to financial losses and eroded trust. Yet, even today, one of the most prevalent solutions to enhance fraud detection is employing more personnel to monitor potential fraud manually. Is there a better, more efficient solution?

The answer to this pressing question can be found in AI/ML-driven data products that clean and curate data to discern unusual patterns, placing human intelligence at the centre to verify the results surfaced by AI algorithms and bolstering the effectiveness of fraud detection.

Mastering a web of information

Data products are the best version of data. They are comprehensive, clean, curated, continuously updated data sets, aligned to key entities such as investment portfolios, customers or suppliers, and can be used by humans and machines broadly and securely throughout the organisation.

Good data products are pre-built with data enrichment to address data quality challenges inherent in first party (collected and owned by the organisation) and third-party data that an organisation collects from external entities such as banking, trading, and regulatory bodies. Third-party data is important because it boosts the value of existing datasets by supplementing or enriching them. Many companies realise their data might be incomplete and incorrect and that external sources, such as address information, are often more accurate than their own entries.

The best data products pair built-in data quality capabilities that standardise and validate data with powerful, ML driven ID linkage and enrichment capabilities that match first-party data with the best external reference data (third-party data), allowing organisations to consolidate various data sources for a comprehensive view, enhancing trend detection and identifying potential issues that might otherwise go unnoticed.

Enhancing fraud detection with advanced data products 

Fraud detection in financial services can greatly benefit from AI-powered data products. Trained on meticulously curated data, this AI technology can accurately identify anomalies and subtle irregularities and swiftly flag potentially fraudulent activities that may go unnoticed by traditional rule-based master data management systems.

Consider the following scenario: An organisation gathers data on potential fraudsters from multiple departments. Yet, business leaders are sceptical about the insights from this data because of uncertainties regarding its cleanliness, accuracy, and trustworthiness. Here’s where data products come in. They accurately and reliably detect patterns in the data, including irregularities that may hint at coordinated fraudulent activity across seemingly unrelated accounts, and consolidate, clean, and categorise this data into a centralised repository. This process reveals patterns for easier fraud detection, bolstering trust in the data’s quality and leading to more precise, actionable insights.

Innovating with humans in the loop

Human intelligence plays a crucial role in the process described above. For optimal results, the AI powering data products need humans in the loop to provide feedback on the ML models and validate their outputs.

This human feedback not only helps in refining the models but also continuously improves their outcomes. Over time, these ML models yield increasingly accurate results, minimising false positives. Leveraging AI/ML for the bulk of data cleaning means human resources can focus less on tedious manual processes and more on continuously evolving and fine-tuning broader fraud detection strategies.

Strengthening the UK’s capital markets

Data mastery holds a particularly immense opportunity for the UK’s capital markets landscape. By consolidating data from the likes of investment portfolios, trading activities, and regulatory reporting, financial institutions can proactively monitor and address potential instances of fraud, including unusual trading patterns and/or suspicious transactions.

For instance, data mastery could enable the cross-referencing of trading activities against a particular individual’s investment portfolio and flag unusual activities that could indicate fraudulent behaviour. Such insights, coupled with ML’s pattern recognition capabilities, could then allow authorities to intervene before a significant issue occurs, thereby preserving the market’s integrity.

And it’s not just fraud detection which data mastering can bring benefits to for those in capital markets. Let’s dive into another specific example, say an investment firm not only wants to detect fraud more effectively but also needs better insights into its portfolio investment market and company trends to outperform its competitors and, ultimately, attract more capital from outside investors.

With data mastering, it can connect a growing list of alternative data sources and gain complete, accurate, and continuously updated views of portfolio companies and potential investments with minimal effort from resources. Through this automation, the firm can find better deals and attract more external capital.

Reducing fraud and building trust

In the fast-paced landscape of financial markets, the need for robust fraud detection has never been more important. And so, data mastery emerges as the golden key in the fight against fraud. Through its ability to combine and refine vast volumes of data, this approach empowers different stakeholders with actionable insights that drive better decision making. This presents a significant opportunity to not only enhance the efficiency of fraud detection but also bolster confidence in the market’s overall integrity.

As the financial landscape continues to evolve, data mastery will remain an effective tool to safeguard against continuously evolving forms of fraud. By embracing this transformational approach to data mastering, the UK’s financial markets can enter an era of heightened vigilance, transparency, and trust, ensuring a resilient and thriving future financial ecosystem for all.

The future of fraud detection with AI-powered data products

In conclusion, the implementation of AI/ML-driven data products in financial services represents a significant stride towards improved fraud detection. These technologies not only streamline the process of data analysis but also enhance the overall accuracy of identifying fraudulent activities, enabling organisations to institute proactive measures in combating cybercrime.

When combined with human intelligence for continuous tuning and validation, AI-powered data products can equip organisations with the tools necessary to ensure the integrity of their financial operations, thereby fostering a more secure and trustworthy business environment. It’s time for industry leaders to embrace advanced data products and step into the future of fraud detection.