Tamr: Unlocking the future of fraud detection in FS

Suki Dhuphar, Head of EMEA at Tamr, says: "Identifying fraudulent activities has grown in complexity, and on top of this, the methods for spotting and combatting them are outdated"
We speak to Suki Dhuphar, Head of EMEA at Tamr about what financial services can do to better detect fraud, and the barriers to achieving this

Head of EMEA at Tamr, Suki Dhuphar, speaks to FinTech Magazine about the challenges faced in the financial industry when it comes to detecting fraud, and how the process can be improved by leveraging AI-powered data products. 

What challenges does the financial industry face in detecting fraud?

A pressing challenge that the financial services industry faces is how to detect cybercrime. Identifying fraudulent activities has grown in complexity, and on top of this, the methods for spotting and combatting them are outdated. 

The traditional strategy of employing more manpower to detect fraud simply isn’t enough, and this can leave financial service providers vulnerable to attack. 

This only creates more challenges, including eroding trust in providers to financial losses for all parties involved... apart from the cybercriminals.

How can AI-powered data products transform fraud detection in financial services?

To combat increasingly sophisticated fraud, financial service providers must work smarter. The key lies in harnessing AI-powered data products. 

These data products represent the pinnacle of data quality, offering comprehensive, clean, curated, and continuously updated datasets.

AI algorithms excel at cleaning and organising data to uncover uncommon patterns, enhancing fraud detection. AI-driven data products are a game-changer for fraud detection in financial services. 

Trained on meticulously curated data, they excel at identifying subtle anomalies and swiftly flagging potentially fraudulent activities that traditional master rule-based systems might miss. Using human intelligence to verify the results of the AI significantly bolsters the effectiveness of AI’s performance.

What makes them particularly great for the financial services industry is that they can be tailored to an organisation’s key entities from customers to suppliers, and more. 

Good data products have data enrichment built in, which addresses data quality challenges from both internal and external data sources. 

As such, they have the power to provide oversight of the complex web of information within the business, making it easier to spot trends in unusual behaviour across all areas of the company and reducing the possibility of fraud on a vast number of levels.

So how can this work in practice?

Imagine a theoretical scenario where a financial services provider compiles information on possible fraudsters from multiple departments. 

Yet, owing to concerns about its overall trustworthiness, there's a prevailing scepticism among business leaders regarding the value of the insights derived from this data. Here's where data products come in.

They integrate the data from different departments to accurately and reliably detect patterns, such as irregularities that may indicate coordinated fraudulent activity across seemingly unrelated accounts, and then consolidate, clean, and categorise this data into a centralised repository. 

This data cleaning process streamlines the task of fraud detection by promptly and effectively uncovering patterns, not only bolstering confidence in the data's reliability but also generating more precise and actionable insights.

What role does human intelligence play in AI-powered data products?

Human supervision is integral to AI technology’s success. Human feedback refines the machine learning models to enhance the quality of the data that the AI is trained on to create trustworthiness in the output.

Human feedback allows ML models to evolve, increasing the accuracy of the results that the AI produces and minimising any faulty outcomes, such as biases in what the AI produces. 

Leveraging AI to assist in combatting cybercrime frees humans to focus on more strategic activities, while AI takes on the more tedious, manual data cleaning tasks that are involved in data management. 

In other words, humans can be redirected towards developing and enhancing innovations around fraud-detection strategies.

What is the call to action for industry leaders regarding AI-powered data products and fraud detection?

The use of AI-driven data products within financial services is a critical step towards better fraud detection. As the financial services landscape continues to evolve, data mastering will remain an effective tool to safeguard against the continuously changing forms of fraud. 

By embracing this transformational approach to data management, financial service providers can enter an era of heightened vigilance, transparency, and trust, ensuring a resilient and thriving future financial ecosystem for all.

Second, make sure that human intelligence is at the heart of AI transformation. Not only is this key to the successful transformation of fraud detection within financial services but also has great opportunities for the overall efficiency of an organisation.


For more insights from FinTech Magazine, you can see our latest edition of FinTech Magazine here, or you can follow us on LinkedIn and Twitter.

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