By Rob Meakin, Director, Fraud and Identity, Creditinfo
Financial fraud is surging worldwide, threatening digital economies in every stage of their development. The economic impact of global financial crime is estimated to be a staggering $5 trillion annually. According to the 2024 Nasdaq global financial crime report, scams and bank fraud schemes alone accounted for $485.6 billion in damages last year. Companies today confront a broad range of risks, from shrinking profits and reputational fallout to increasing data breach exposure.
Several factors are fuelling this rapid rise in fraud. The digitisation of financial services has expanded the avenues for fraudsters to exploit. The growth of online identity data has expanded attack surfaces, offering cybercriminals more ways in.
At the same time, bad actors are adopting advanced technologies such as artificial intelligence (AI), machine learning (ML) and automation to accelerate their schemes and avoid detection more efficiently. AI plays both sides, streamlining operations for businesses while also enabling a new wave of complex fraud tactics, from deepfakes and synthetic identities to hyper-personalised social engineering and advanced impersonation schemes.
Alarmingly, more than two-thirds of financial institutions say they aren’t equipped to respond to these evolving threats.
The broader cost of fraud
Financial loss is just one part of the story. Fraud also chips away at brand trust, increases the risk of data breaches, and can lead to serious legal and regulatory fallout. To counter rising threats, organisations must also meet strict compliance obligations, including Anti-Money Laundering (AML) registration and rules around data privacy and consent, without complicating the customer experience or increasing operational costs.
A fresh approach to fraud detection
Rising fraud impacts economic stability globally and locally. The UK government’s Plan for Change recognises that defeating fraudsters requires worldwide collaboration and smarter, more agile strategies. Yet, current security frameworks remain fragmented to address the realities of diverse markets.
Emerging economies often lack robust fraud prevention controls, leaving them vulnerable to hackers. But their smaller digital infrastructures make them less appealing as targets for financial crimes. There is a progressive drive towards driving digitisation as a stimulus for economic growth and improving access to financial and other products to underserved populations.
Conversely, mature markets benefit from more advanced defences, but their vast digital ecosystems and fragmented identity data create new openings for bad actors to exploit.
Going local to fight global fraud
Given the growing sophistication of cybercrime, combatting fraud at scale requires organisations to build local intelligence that enables precise identity verification and trust.
Organisations can more effectively detect fraud and build identity trust by using data from a wide range of local sources and adjusting fraud prevention strategies to reflect market-specific risks. This approach also helps preserve the customer experience and supports broader financial inclusion.
Enhancing intelligence through combined data sources
Fighting fraud starts with building the intelligence needed to establish trust and verify identities. This is where localised data makes a real difference.
By combining credit bureau information with government registries and digital signals, organisations can correlate multiple identity attributes and risk indicators to assess fraud risk and enable real-time trust decisions, establishing a far more hostile environment for bad actors.
Credit bureau data linked to the presented identity can help evaluate risk and reliability across four key dimensions:
- Bureau footprint: data from multiple reporting organisations
- Activity history: consistent payment patterns
- Data consistency: stability of personal information
- Application velocity: recent credit applications frequency
These insights, paired with government and registry data, strengthen identity verification and fraud detection without compromising user experience.
Tailored strategies for compliance and inclusion
Organisations must tailor fraud strategies to each market’s specific needs and maturity levels to build effective defences. In many emerging economies, the lack of formal credit history makes it challenging to verify identities without excluding legitimate users.
Localised data and market-specific tactics help overcome this barrier, making it possible to extend services to underserved groups while maintaining security and minimising risk.
These targeted approaches also support compliance. For example, AML regulations require organisations to “identify, assess, and understand the money laundering and terrorist financing risk to which they are exposed.”
Using localised data and market-specific tactics allows organisations to meet these requirements by aligning fraud prevention measures with regional threat intelligence.
Conclusion
As global financial crime rises, it presents new challenges in detecting fraud, verifying identities and staying compliant. Regional differences in infrastructure, maturity and risk complicate these efforts.
To tackle these threats effectively, businesses must take a more localised approach alongside global sources of trust and risk insight. By combining credit, government and digital data and adapting fraud strategies to each market, they can strengthen intelligence, manage risk and remain compliant. This approach improves security, expands access to financial services and supports greater financial inclusion around the world.
