Automation

Economic abuse is a hidden yet damaging form of control, affecting thousands, particularly during financial or domestic crises. Financial institutions have a responsibility to address this issue and have been working hard to address the needs of their vulnerable customers. But balancing customer protection with ethical and operational challenges is complex. A growing concern is abusive messages, where perpetrators use reference fields that accompany payments to send harmful or controlling remarks to recipients.

Abusive Payment References: A Growing Concern

Abusers use these payment references to send profanities, threats that are both obvious and veiled, or even phrases like “I love you” – three seemingly harmless words but potentially triggering for victims. Some perpetrators exploit the system further by sending “string messages,” where multiple small payments are used to create longer sentences or abusive communications, even going so far as sending one letter at a time for one penny. This type of tactic deliberately takes advantage of payment references, making it challenging for detection systems to catch abusive intent. Such behaviours highlight the need for financial institutions to adapt their detection methods continuously.

The implications of unchecked abusive messages are significant. Victims often experience heightened emotional distress, leading to a loss of confidence in financial systems. As abusers find increasingly sophisticated ways to manipulate payment references, financial institutions must constantly innovate their detection methods to stay ahead.

Ethical Challenges in Customer Protection

Efforts to protect victims raise ethical dilemmas:

1. Balancing Privacy and Monitoring: Banks must navigate the fine line between monitoring transactions and respecting customer privacy. Overreach could lead to concerns about excessive surveillance and erode trust. Should banks be proactively monitoring accounts for this type of activity or rely on recipients to complain before they do something. Even where a recipient doesn’t complain, proactive engagement by banks has the potential to wrongly tip-off perpetrators.
2. Intent Ambiguity: Differentiating abusive messages from harmless banter is challenging. While context might clarify intent, assessing it without invading privacy is a significant obstacle.
3. Account Closures: Closing an abuser’s account may inadvertently harm victims, cutting off critical payments such as child support or maintenance payments. This creates a conundrum for banks seeking to penalise abusive behaviour without collateral damage.

In the UK, Basic Bank Accounts add further complexity, as these accounts are protected under specific regulations that make them difficult to close, even in cases of misuse. UK Finance has proposed changes to the 2015 Payment Accounts Regulations to cover payment references causing harm and harassment, ensuring institutions can act more decisively, even with Basic Bank Accounts.

Lessons from Leaders in Addressing Economic Abuse

UK Finance’s “From Control to Financial Freedom” report highlights actionable solutions, such as:

• Blocking Payment References: Empowering victims to block specific references or hide abusive messages reduces trauma while preserving necessary transactions.
• AI Detection: Leveraging AI to identify patterns of abuse, such as repeated low-value transactions or coded language, enhances detection capabilities.
• Collaboration: Partnering with domestic abuse charities allows banks to better understand victim-survivors’ needs and refine their response strategies.

Internationally, the Commonwealth Bank of Australia (CBA) has set an example through its “Next Chapter” initiative. By training staff to recognise signs of abuse, partnering with specialist organisations, and implementing tools to prompt senders to reconsider abusive messages, CBA has fostered greater accountability and deterrence. These proactive measures not only protect victims but also create a safer financial environment overall.

Industry guidance will continue to develop in collaboration with banks and regulators to ensure a more consistent approach to managing customers who send or receive abusive payment references. The UK is behind Australia in its development of initiatives to tackle this problem but remains ahead of many other countries now starting to address similar issues.

The Role of AI in Abuse Detection

AI technologies can be instrumental in addressing abusive payment references:

• Pattern Recognition: Advanced algorithms can identify trends, such as frequent low-value payments paired with hostile language, to flag potential abuse.
• Natural Language Processing (NLP): NLP enables systems to detect subtle or coded language, which traditional keyword detection might overlook, or where numerals are used to replace letters.
• Continuous Learning: Machine learning models can evolve to recognise new forms of abuse as they emerge, staying ahead of abusers’ tactics.

AI’s adaptability makes it a powerful tool, but it must be implemented responsibly to avoid unintended consequences.

Addressing Ethical Concerns with AI

The use of AI in detecting economic abuse raises its own ethical questions:

• Data Privacy: Analysing sensitive payment data must comply with privacy laws and maintain customer trust.
• Bias Prevention: AI models must be rigorously tested to prevent inaccuracies that could unfairly penalise individuals or fail to identify genuine abuse.
• Transparency: Banks should communicate openly about how AI is used in abuse detection, ensuring customers understand its purpose and limitations.

By addressing these concerns, financial institutions can deploy AI solutions effectively and ethically.

Practical Solutions for Protecting Victims

To safeguard victims effectively, financial institutions should consider:

1. Customised Protections: Allow victims to block specific senders or hide payment references, enabling them to regain control of their financial interactions.
2. Staff Training: Equip employees with the skills to recognise and handle cases of economic abuse sensitively and effectively.
3. Legislative Advocacy: Push for changes in laws to address gaps, such as enabling the closure of abusive Basic Bank Accounts without undue restrictions.
4. Cross-Industry Collaboration: Promote partnerships with charities, policymakers, and technology providers to develop comprehensive solutions that address the multifaceted nature of economic abuse.

How MBC Can Help

MBC helps financial institutions tackle economic abuse with tailored solutions. Recently, we supported a high street bank to implement the blocking of abusive payment references, advising on compliance, data analysis, and enabling cross-functional collaboration to successfully deliver the project. Our expertise bridges the gap between regulatory requirements and practical implementation, ensuring that banks can protect vulnerable customers effectively.

Beyond technical support, we guide organisations in nurturing collaborative frameworks that prioritise customer safety and ethical practices. Our approach ensures financial institutions can adapt to evolving challenges while maintaining ethical standards and open communication.

Conclusion

Economic abuse presents complex challenges that require innovative and ethical responses. Abusive payment references are just one aspect of a broader issue that demands attention from financial institutions. By learning from leaders, leveraging advanced technologies, and adopting a customer-centric approach, banks can make significant strides in protecting vulnerable individuals.

Partnering with MBC ensures a strategic, effective approach to tackling these challenges, empowering financial institutions to support their customers while upholding the highest ethical standards.

The age of AI is truly upon us, marking a pivotal shift from the digital-first and mobile-first eras that have defined organisational innovation over the past two decades. In these earlier phases, businesses adapted to new technologies to meet evolving customer expectations and operational efficiencies. Now, AI promises not just adaptation but real transformation, unlocking opportunities to redefine strategies, enhance customer experiences, and drive growth. For organisations, especially in financial services, this AI era demands a blend of foresight, agility, and close collaboration between leadership and technology teams to navigate an increasingly AI-driven landscape.

Data-Driven Transformation: Lessons from Klarna

Klarna, a leading global payments provider, illustrates the transformative potential of AI in driving organisational strategy. Recent advancements include their AI-driven customer service assistant, which now manages two-thirds of customer service interactions, handling the equivalent workload of 700 full-time agents while reducing repeat inquiries by 25%. Similarly, Klarna’s internal AI assistant, Kiki, supports over 2,000 daily employee queries, streamlining internal processes and fostering transparency. This adoption of AI has not only enhanced efficiency but contributed to a 27% revenue growth in early 2024, demonstrating the financial benefits of aligned AI initiatives.

However, Klarna’s success isn’t only about technology. It’s about strategy. Klarna’s CEO, Sebastian Siemiatkowski, recently highlighted the company’s commitment to reinvesting AI-driven efficiencies into employee development, such as accelerating salary increases.

By embedding AI across operations and aligning initiatives with long-term objectives, the company exemplifies how financial services can leverage AI strategically. For banks and traditional institutions, which may lack Klarna’s agility, the key lies in structured and incremental adoption.

Aligning AI with Strategic Goals

AI initiatives must be closely tied to organisational strategy to ensure measurable outcomes. This alignment is particularly critical for banks, where regulatory requirements and legacy systems present unique challenges. Leaders and technology teams must collaborate to:

1. Define Strategic Objectives: Identify how AI aligns with key goals such as improving customer experience, enhancing operational efficiency, or innovating products and services.
2. Prioritise Feasible Projects: Incumbent institutions must balance ambition with realism, focusing on projects with tangible benefits that align with regulatory and operational constraints.
3. Mitigate Risks: Establish governance frameworks to ensure AI implementations comply with data privacy and ethical standards while minimising operational disruptions and potential misuse.
4. Ensure Human Oversight: Maintain a human-in-the-loop approach to ensure quality control. Whilst the AI can help accelerate productivity, the monitoring and validating of outputs ensures critical decisions are still made by humans. In a recent project, MBC carried out structured monthly data reviews to analyse and identify false positives, which was a vital step to not only keep a close eye on outputs but also feedback and iteratively improve existing models.
5. Foster Cross-Functional Collaboration: Cross-departmental teams that integrate compliance, technology, and strategy functions are critical for cohesive implementation.

Opportunities for Banks and Financial Institutions

Unlike fintechs, many banks operate within a legacy and often monolithic framework that makes rapid AI adoption more complex. However, they possess fundamental strengths such as vast customer bases, established trust, and access to large datasets. By adopting an incremental approach, banks can:

• Leverage Data Assets: Use AI to derive actionable insights from historical and transactional data to personalise offerings and improve risk assessments.
• Automate Routine Processes: Focus on AI-driven automation in areas such as loan processing, fraud detection, and customer service to free up resources for higher-value tasks.
• Collaborate on Industry Initiatives: Banks can participate in collaborative frameworks, such as shared AI platforms or industry-led consortiums, to accelerate innovation while distributing costs and risks.

Guidelines for AI-Driven Growth

Financial institutions should adopt a structured approach to AI that includes:

1. Start with Strategic Use Cases: Identify high-value opportunities where AI can make an immediate impact, such as supporting customer service, enhancing fraud detection, or improving loan approval times.
2. Invest in Infrastructure: Modernise legacy systems to ensure compatibility with AI technologies and support data scalability.
3. Develop Internal Expertise: Build in-house AI capabilities through training and talent acquisition, ensuring teams can deploy and manage solutions effectively. Partner with companies like MBC to supplement this expertise with experience in quality digital delivery.
4. Monitor and Refine: Implement KPIs to measure AI’s impact, iterating on projects to optimise results.
5. Balance Innovation with Governance: Establish clear policies around AI ethics, data privacy, and compliance, creating a foundation of trust with customers and regulators.

Embracing Future Innovations

While current AI capabilities like predictive analytics and automation are transformative, emerging technologies such as generative AI and autonomous decision-making systems will drive the next wave of innovation. Banks and financial institutions should remain proactive in exploring these advancements while carefully managing risks.

Agentic AI, which allows systems to act autonomously, offers exciting possibilities for decision-making in complex scenarios where multiple agents can interact and supervise one another, either in a peer-to-peer or hierarchical structure. By adopting pilot programmes and fostering a culture of experimentation, incumbents can stay ahead of these developments.

How MBC Can Help

At MBC, we’ve been helping organisations navigate the complexities of digital adoption for years, and AI is just the next step in digital’s evolution. For financial services, we can help align AI initiatives with strategic goals while addressing the unique challenges posed by legacy systems and regulatory frameworks. Whether it’s conducting feasibility studies, developing AI roadmaps, or ensuring quality delivery, our tailored solutions ensure measurable outcomes.

Partnering with MBC enables organisations to leverage AI’s potential effectively, positioning themselves for sustainable growth in the digital era.

Conclusion

The digital era demands a strategic approach to innovation, especially for financial services. Klarna’s success highlights the transformative potential of AI when aligned with strategic goals, but the lessons apply equally to banks and incumbents. By fostering collaboration, adopting structured approaches, and embracing emerging technologies, organisations can steer their direction confidently.

With the right partnerships and strategies, financial institutions can navigate the challenges of the digital era, driving growth and maintaining market competitiveness. Connect with MBC today to explore how we can support your AI transformation journey.