
1. The Power of AI in Shaping Financial Services’ Future
As AI capabilities rapidly expand within the financial sector, they bring transformative possibilities that could redefine customer engagement and business operations. With almost every global business leader considering AI crucial to organisational success over the next five years, the momentum in AI adoption is undeniable. Automated solutions, from chatbots to robo-advisors and trading algorithms, have already changed how financial services are delivered. But the potential for AI goes far beyond: fully automated, client-facing services—such as personalised underwriting of insurance policies, customised portfolio management, and tailored financial advice—offer financial institutions a unique opportunity to enhance the customer experience, improve financial management and customer welfare, drive growth and create sustainable revenue streams.
2. Opportunities of AI in the Finance Industry
AI offers immense potential to transform services and create measurable value for both customers and financial institutions. With almost 70% of organisations ramping up their investments in AI and Generative AI specifically, the financial industry is well-positioned to lead in AI-driven innovation.
- Smart Credit Offerings: Lenders can leverage AI to create smart credit options that prioritise ethical standards, user-targeted defaults, and transparent risk assessments. AI-driven fraud detection also enables financial institutions to monitor large volumes of transactions, reducing potential losses and bolstering trust in the lending process. For instance, over 90% of UK financial institutions are already leveraging Predictive AI, primarily for fraud detection, back-office functions, and risk management.
- Personalised Investment Advice: AI enables advisory services to deliver tailored investment advice by analysing individual risk profiles and behavioural patterns, providing clients with portfolio recommendations aligned with their unique needs.
- AI-Powered Underwriting: In insurance, AI-based assessments improve the accuracy and efficiency of underwriting policies, resulting in products that are more relevant and affordable for consumers.
The integration of AI facilitates a truly personalised approach that improves customer experience. This is not only because clients feel satisfied and supported, but also because they experience tangible financial benefits. These benefits can include:
- Enabling clients to obtain loans with fairer terms and lower risk of debt accumulation,
- Helping them make more informed investment decisions that contribute to improved portfolio performance, and
- Providing access to affordable insurance options that avoid overpaying for unnecessary coverage or facing inadequate protection.
Despite these possibilities, there is still considerable work to be done before AI models allow for proper and responsible assessments.
3. Potential Consequences of AI Misuse
Financial firms must be vigilant, as the potential misuse of AI not only risks harming individuals but also impacts the organisation’s reputation and long-term success. Around 80% of consumers surveyed about AI application express concerns over the potential misuse of AI in financial services, highlighting the need for ethical frameworks and transparency
Without an appropriate design and control, AI systems can lead to significant ethical and operational risks, from discrimination and manipulation of information to coercive defaults and deceptive marketing techniques. If left unaddressed, these threats can erode customer confidence, damage brand loyalty and expose financial institutions to regulatory sanctions. Not to mention the financial damage it can cause to consumers.
4. Existing Regulations for Responsible AI
In response to these challenges and threats, regulatory frameworks such as the EU AI Act provide essential rules to govern AI’s applications. The EU AI Act outlines critical standards for acceptable AI practices,ranging from appropriate and prohibited practices, potential risks, disclosure requirements for transparency, obligations of companies and developers, fundamental rights of users and corporate liability with the aim of protecting people from potential risk, abuse and misconduct. Additionally, the OECD AI Principles reinforce these standards, advocating for AI that aligns with societal values and contributes positively to the public good. These regulatory frameworks are some of the reasons why more than 80% of decision makers in the financial sector believe that a collaboration with UK regulators would be beneficial.
5. Harnessing AI in Financial Institutions
For financial institutions, adhering to regulatory and ethical standards is not merely a compliance requirement in a highly regulated sector. It represents an opportunity to provide equitable and accessible services while safeguarding customer rights and expectations. Integrating ethical principles into AI-driven processes enables firms to differentiate themselves from competitors, fostering the development of AI systems that empower customers to make informed decisions free from manipulation or unnecessary complexity. This is an ideal time to implement such principles, as over 70% of Generative AI initiatives are currently in the proof-of-concept or pilot phase, allowing room for adaptation and improvement.
This evolving landscape offers firms the chance to shape the future of AI in finance by embracing solutions that deliver value responsibly and foster long-term, mutually beneficial relationships with clients. It positions the industry as a leader in promoting a trustworthy, ethical, and impactful future.
6. Enhancing AI in Financial Services through Behavioural Science
a. Training AI Models for Finance: Ensuring Fair and Inclusive Data
The effectiveness of AI in finance relies heavily on the quality and inclusivity of the data it is trained on. For AI systems to deliver fair and accurate outcomes, it is crucial that they are built on datasets that are diverse, representative, and free from historical biases related to factors such as ethnicity, geography, and socio-economic status. By applying behavioural science principles to the model training process, financial institutions can proactively identify and mitigate these biases, creating AI models that support equitable financial outcomes. This commitment to inclusive data practices helps to ensure that AI systems benefit all customer segments, fostering both trust and social responsibility in financial services.
b. Credit Scoring: Promoting Fairness and Inclusive Data in Financial AI Applications
In credit scoring, fairness is paramount, as biased outcomes can unfairly restrict access to financial services and undermine an institution’s reputation. AI-driven credit models must be designed to avoid biases that could disadvantage certain demographics, potentially excluding groups of profitable, creditworthy customers. By incorporating diverse financial behaviours, including those of underrepresented groups like young or unbanked individuals, financial institutions can develop more equitable loan approval algorithms that go beyond traditional credit scores. This inclusive approach not only enhances fairness but also broadens the institution’s reach, creating opportunities to engage a wider customer base that might otherwise be overlooked, ultimately strengthening customer loyalty and financial inclusion.
c. Advertising: Establishing Ethical Standards in AI-Driven Financial Services
To maintain trust and protect consumers, financial companies must ensure the ethical application of AI. By aligning AI use with ethical norms and societal values, firms can avoid the negative repercussions of manipulative practices. For example, in automated financial advertising, companies can implement mechanisms to prevent AI from exploiting consumer vulnerabilities, such as heavily promoting overdrafts as accessible income, presenting BNPL products as “free money”, or positioning complex investment products as low-risk options. This approach not only protects customers’ financial wellbeing but also strengthens brand integrity.
d. Investing: Minimising Corporate Bias in Financial AI
As financial firms deploy AI to enhance profitability, it is essential to prioritise ethical practices that benefit consumers and uphold trust. Behavioural science insights can reduce the influence of profit-driven biases within AI systems. Investment algorithms can be transparently structured to recommend products based on client needs and suitability, rather than favouring high-fee options. This ensures that AI-powered financial advice remains consumer-focused, providing fair and beneficial recommendations that align with clients’ best interests.
e. Embedded Finance: Ensuring Inclusive Financial AI for Societal Welfare
AI systems in the financial sector, especially within embedded finance, should be designed to enhance social wellbeing and reduce inequalities. Guided by behavioural science, firms can create inclusive AI tools embedded directly within non-financial services, ensuring fair and easy access to financial resources across all segments of society. The integration of AI-driven budgeting and savings tools with popular retail or utility apps can offer personalised financial guidance to diverse user groups. To truly foster inclusivity, this approach must be accessible to all segments of the population, including the most disadvantaged, empowering a broader base of customers and promoting financial literacy, resilience, and greater access to essential financial services across varied socio-economic groups.
7. Guiding Ethical AI Integration with Behavioural Science
The integration of AI in financial services holds enormous potential to drive growth, improve client relationships, and enhance financial resilience across diverse client bases. Yet, realising this potential requires a deep understanding of the behavioural impacts AI can have on consumers.
At Behavioural Finance Consulting, we specialise in applying behavioural science to ensure that AI implementations in finance are both ethical and effective, aligning technology with the needs and values of your customers. Whether it’s developing fair credit assessment tools, designing consumer-friendly investment algorithms, or embedding financial guidance seamlessly within everyday services, our expertise can help you navigate these complex challenges.
If your organisation is looking to maximise the positive impact of AI while fostering client trust and financial prosperity, we invite you to reach out. Our team is here to provide the strategic guidance and insights needed to responsibly and successfully implement AI solutions that enhance financial services for all.
References
International Telecommunication Union 2024, “AI for Good. Impact Report”.
https://www.itu.int/net/epub/TSB/2024-AI-for-Good-Impact-Report
UK Finance 2023, “The Impact of AI in Financial Services: Opportunities, Risks and Policy Considerations”.
https://www.ukfinance.org.uk/policy-and-guidance/reports-and-publications/impact-ai-in-financial-services-opportunities-risks
Accenture 2024, “Banking Top 10 Trends for 2024. Banking on AI”.
https://www.accenture.com/us-en/insights/banking/top-10-trends-banking-2024
World Economic Forum 2024, “Governance in the Age of Generative AI: A 360o Approach for Resilient Policy and Regulation”.
https://www.weforum.org/publications/governance-in-the-age-of-generative-ai