
Getting the Most Out of Your AI by Improving the Quality of Your Data
AI is transforming how payments businesses, financial institutions, and corporates operate by improving efficiency, fraud monitoring, and customer experience. The value AI delivers hinges on the quality of the data it processes, a key theme discussed at Cuscal’s Curious Thinkers 2024 event.
AI applications are already making an impact in financial services with customer service bots, predictive analytics, and fraud monitoring systems. According to the Wall Street Journal, businesses are already using AI to provide personalised offerings to clients, identify clients, and identify fraud in real-time.
AI adoption in financial services is accelerating, with a recent ASIC review identifying 624 AI use cases across banking, credit, insurance, and financial advice. Over half (57%) were developed in the past two years or still in progress, and 61% of licensees plan to expand AI use within the next year. This momentum reflects the sector’s commitment to leveraging AI-driven solutions to stay competitive and deliver enhanced value to customers.
These advancements are delivering measurable benefits, but they are only as effective as the data that supports them. As IBM notes, poor-quality data can undermine AI initiatives, leading to unreliable outputs and heightened business risk.
The Opportunity for Payments Businesses
Generative AI is reshaping industries, offering both economic and operational advantages. J.P. Morgan Research estimates that generative AI could boost global GDP by as much as 10%, adding between $7–10 trillion to the economy. Over the next few years, it is also expected to drive a significant surge in workforce productivity.
Closer to home, AI adoption is delivering tangible economic benefits for Australian businesses. Recent research from Microsoft and Mandala estimates that by 2035, the AI economy could generate $18.8 billion in annual revenue for Australia. Additionally, businesses investing in generative AI are seeing a $3.50 return for every $1 spent, often within just 14 months.
The success of these AI systems, however, hinges on the quality of the data driving them. AI tools process enormous amounts of datasets to generate insights. When those datasets are incomplete, inaccurate, or poor quality, the results can be unreliable—or worse, damaging.
The Quality Question
Payments providers and financial institutions handle vast amounts of data every day. From transaction histories to customer profiles, this data feeds into AI models that help businesses make smarter decisions and optimise operations.
When data quality is neglected, the consequences can ripple across the organisation and may contribute to:
- Misjudged risk assessments, leading to financial losses or missed opportunities
- Flawed market insights, resulting in poor investment decisions
- Inaccurate customer recommendations, damaging trust and satisfaction
As AI adoption accelerates, so too does the importance of ensuring data is accurate, complete, and relevant. Details that might have been overlooked in the past—such as timestamps or metadata—now play a critical role in delivering reliable AI outputs.
Steps to Improve Data Quality
Investing in data quality is critical for payments businesses aiming to leverage AI effectively. Taking action in these areas can make a significant difference:
1. Data cleaning: Removing or correcting errors ensures accuracy and improves performance. IBM highlights how this step reduces risks and enhances results.
2. Data governance: Establishing clear processes for collecting, storing, and managing data creates consistency and reliability.
3. Regular monitoring: Ongoing audits help identify and fix data quality gaps before they affect AI outputs.
By addressing these challenges, organisations can unlock smarter decision-making, better customer outcomes, and a competitive edge. Because high-quality data isn’t just a technical asset—it’s a strategic one.
The Future of AI in Payments
The payments industry is evolving rapidly, with AI playing an increasingly important role in shaping it’s future. Organisations ready to invest in data quality will be the ones leading the way.
References
Australian Securities & Investments Commission, ‘Beware the gap: Governance arrangements in the face of AI innovation, Report 798’, Australian Securities & Investments Commission website, report, October 2024, accessed 10 January 2025,<https://download.asic.gov.au/media/mtllqjo0/rep-798-published-29-october-2024.pdf>
Belle, L 2024, ‘These New AI Bots Will Do Just About Anything for You’, Wall Street Journal, article, 24 August 2024, accessed 10 January 2025,<https://download.asic.gov.au/media/mtllqjo0/rep-798-published-29-october-2024.pdf>
Bousquette, I 2024, ‘Visa Has Deployed Hundreds of AI Use Cases. It’s Not Stopping’, Wall Street Journal, article, 1 November 2024, accessed 10 January 2025, <https://www.wsj.com/articles/visa-has-deployed-hundreds-of-ai-use-cases-its-not-stopping-4febe1b4>
J.P. Morgan, ‘Is generative AI a game changer?’, J.P. Morgan website, global research, 14 February 2024, accessed 10 January 2025,<https://www.jpmorgan.com/insights/global-research/artificial-intelligence/generative-ai>
|Microsoft, ‘New research identifies Australia’s most promising opportunities in the new global AI economy’, Microsoft website, features, 7 November 2024, accessed 10 January 2025, <https://news.microsoft.com/en-au/features/new-research-identifies-australias-most-promising-opportunities-in-the-new-global-ai-economy/>
Rogers, J 2024, ‘What Is Data Cleaning?’, IBM website, article, 29 November 2024, accessed 10 January 2025,<https://www.ibm.com/think/topics/data-cleaning>
Important Information: Information in this article is current as at 19 February 2024 and is subject to change. This article is provided for general information purposes only and does not have regard to the situation or needs of any reader and must not be relied upon as advice. Before acting on this information, consider its appropriateness to your business Cuscal Limited ABN 95 087 822 455.