FinTech in 2023: unlocking the challenges with Fraud Data Exchange

● The structure and language: so when you get the data it’s in the format you need it in already – This should lend itself to being hard coded and machine processable – fundamentally, “straight through processed” data

● Exactly the right information that should be inserted into each data field

● As consistent a way as possible of sending the information real impact of bad data manifests itself; a lack of trust in the data being received, ending up in people continually having to re-check and verify it. This ultimately drives increased costs and time before you’ve even done anything with the data, and in some instances, a financial cost, like a penalty or fee for late or inaccurate data submissions.

FinTech can help play a key role here, but like any problem you have to try and diagnose the underlying causes. Some examples might include:

● A wide range of sources of data in different systems (and if you’re at a large organisation, don’t underestimate the potential complexity and number of different data systems), in different locations, and internal vs externally managed systems

● Different assumptions around the data: either variations in how the data is cut (for example, different “as at” dates), or when manipulation of the information excludes a subset of the data for some reason

● Different formats: for example, languages, structure, formatting

– The typical, albeit simple, example here is when you receive data in an Excel spreadsheet that has a series of dates in one column. These dates might be in the wrong numerical format and need to be adjusted for the recipient to read correctly

● Variability in overall data quality resulting from manual entry or “human oversight”. How often do we see examples in the press from large banks where something has gone wrong from an incorrectly inputted data error?

● Inconsistent usage: instances where a customer has too many fields to select from and the chance to potentially put the same data into different fields. It could alternatively be a situation where the data attribute is defined differently by different people; for example: “length of customer relationship” could mean when the customer first had a relationship with a bank or when it first obtained a current account with the bank (the two not always being the same)

The list goes on but it’s clear FinTech can be at the heart of this and where creating standard can play a key role, by agreeing:

● That it can evolve as the outcome(s) evolves (i.e. maximising the economies of scale across an entire business of having standardised data)

More and more of what I’m hearing is support and demand for standards to play a role in combatting fraud through improving data sharing – for large organisations, businesses, end users and regulators.

As we think about the challenges around fraud in 2023 and beyond, it is clear that standards have a key role to play and FinTech is at the heart of making that happen: both from an implementation and a “galvanising industry” perspective. The future isn’t about the process of sharing data, the future is about what we need to do with good quality data – and standardising fraud data is a key component to enabling that. If FinTech can overcome this challenge, we can get to combatting fraud sooner and earlier in the payments process.

Matthew Cheung CEO,

The financial markets sector has seen considerable investment in digital transformation and automation projects in the last few years. This, combined with the COVID Pandemic which permanently changed working habits, has helped fuel the rapid rise and use of chatbots.

We’re now seeing organisations start to explore chatbots to streamline operations and improve customer experience. Automation of tasks, real-time market insights, and personalised recommendations enhance efficiency and satisfaction. The adoption of these technologies will become crucial in staying competitive and meeting market demands. The growing importance of chatbots in our sector will transform the financial landscape in the next couple of years.

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To understand why chatbots are already becoming more prevalent in the capital markets space let’s understand the key benefits.

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