Why Community Financial Institutions Need an AI Operating System

The financial services landscape is at an inflection point. Recent industry data shows AI adoption by UK financial institutions doubled from 32% to 63% between 2023 and 2024, with over 80% of banking executives now viewing AI as a business opportunity. Yet this transformation is uneven – while large banks invest heavily in AI, community financial institutions often lack the infrastructure to compete.

Working with over 300 credit union staff across Scotland, we’ve observed a critical reality: small teams are spending much of their time manually maintaining, distributing, and correcting data – constraining their bandwidth for value-added activities. This isn’t just an efficiency problem; it’s preventing vital community institutions from achieving the economies of scale needed to compete with larger banks.

The U.S. market provides compelling evidence for how AI can level this playing field. Nearly 30% of U.S. community banks and credit unions plan to implement generative AI tools in 2025, with early adopters already seeing significant gains in operational efficiency and member service. Through innovators like Eltropy, even the smallest credit unions are using AI to handle routine queries, process documents, and personalise communications.

At Niovant, we’re bringing this transformation to the UK market. Our thesis is simple: semi-autonomous AI agents will fundamentally elevate productivity by eliminating routine work at scale, giving organisations back time for higher-value tasks. Our platform ingests raw data from core systems, structures it for AI agents, then triggers the actions and workflows that previously consumed staff time.

The Building Societies Association noted in 2024 that mutual lenders are actively exploring how AI can boost staff productivity and customer satisfaction “at scale – but reduce cost” in line with their values. We’re making this possible by building an operating system that helps financial institutions orchestrate AI automations while adapting to shifting goals, regulations, and member needs.

The future of financial services won’t be built on static software, but on adaptive AI systems that give organisations back their most precious resource: time. We’re building that future, starting with the institutions that need it most.

Please reach out to me at lewis@niovant.com to discuss collaboration.

When Finance Meets Real Life

The financial landscape is rapidly evolving, with a growing emphasis on integrating financial services seamlessly into consumers’ daily lives. A new report from Rise, created by Barclays and Rainmaking explores this evolution in their report, “When Finance Meets Real Life.” The report, released as part of The Innovation Spotlight Series looks at the convergence of finance and real-life applications, driven by technological advancements, economic pressures, and regulatory changes.

Key Drivers of Change

The report identifies several key drivers reshaping the financial sector:

1. Economic Pressures: Rising inflation, the cost-of-living crisis, and increasing interest rates are making it harder for individuals and businesses to access credit. These challenges are pushing consumers to become more resourceful, while businesses are shifting focus towards sustainable growth rather than relying on abundant venture funding.
2. Artificial Intelligence and Personalisation: AI is increasingly being adopted across industries, with nearly 18% of global venture funding in the first half of 2023 going to AI-related companies. AI’s potential to transform financial services is immense, particularly in areas like customer experience and regulatory compliance. Personalised financial services, powered by AI, are becoming crucial as consumers demand more tailored and context-specific offerings.
3. Regulatory Catalysts: New regulations, such as the UK’s Consumer Duty and the EU’s Green Deal, are shaping the future of finance. These regulations aim to protect consumers and promote sustainability, while also driving innovation by setting higher standards for financial products and services.
4. Embedded Finance: The embedded finance market, valued at $65 billion in 2022, is expected to grow significantly by 2027. This model, which integrates financial services into non-financial platforms, is revolutionising how consumers access banking services. Examples include the growth of Buy Now, Pay Later (BNPL) services and other point-of-sale financing solutions.

Thriving in a Seamless World

The report also highlights the growing consumer expectation for seamless financial experiences. The report discusses how embedded finance can help banks integrate services more naturally into everyday activities to reduce friction for users. The challenge for financial institutions is not just about offering these services but making them intuitive, timely, and relevant to each customer’s unique needs.

Personalisation and Consumer Engagement

Personalisation in financial services is still catching up compared to other industries. While consumers can personalise products like M&Ms or choose customised content on Netflix, financial services often lack this level of customisation. The report argues for a more sophisticated use of data to predict and respond to individual customer needs, creating a more engaging and relevant banking experience.

Making Money Talks Easier

The report highlights the importance of making financial discussions less intimidating and more accessible to consumers. With rising debt levels and financial stress, financial institutions need to provide empathetic support. This includes using AI and other technologies to simplify interactions and make financial advice more accessible.

Read the full report

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The Role of AI and Cybersecurity in the Financial Sector

Artificial Intelligence (AI) and cybersecurity are revolutionizing the financial sector. As the digital landscape evolves, financial institutions are increasingly relying on AI technologies to enhance security measures, optimize operations, and deliver personalized customer experiences. The intersection of AI and cybersecurity has become crucial for safeguarding sensitive financial data and maintaining trust in the industry. This article will explore how AI is transforming cybersecurity in finance, the challenges involved, and the essential skills needed to thrive in this rapidly changing environment.

The rise of AI in finance

AI technologies, such as machine learning, natural language processing, and robotic process automation, have been instrumental in transforming the financial industry. By automating routine tasks, AI helps financial institutions to streamline operations, reduce costs, and improve efficiency. Furthermore, AI-driven insights enable financial firms to make informed decisions, assess risks, and develop targeted strategies. One of the most significant benefits of AI in finance is its ability to enhance cybersecurity measures. As cyber threats become more sophisticated, financial institutions must adopt advanced technologies to protect their systems and data. By identifying patterns, detecting anomalies, and responding to threats in real-time, AI is an invaluable cybersecurity tool.

The importance of cybersecurity in finance

Cybersecurity is a top priority for the financial sector, as cyberattacks can have devastating consequences. Data breaches can lead to financial losses, reputational damage, and regulatory penalties. Furthermore, cyberattacks can disrupt financial services – impacting customers and the broader economy. The financial industry is particularly vulnerable to cyber threats due to the vast amounts of sensitive data it handles. Personal information, financial transactions, and proprietary data are prime targets for cybercriminals. Therefore, financial institutions must implement robust cybersecurity measures to safeguard their assets and maintain customer trust.

AI enhances cybersecurity for the financial industry

AI offers several advantages for cybersecurity in the financial sector:

1. Threat Detection and Prevention: AI algorithms can analyze vast amounts of data to identify patterns and detect anomalies indicative of cyber threats. By continuously learning from new data, machine learning models improve their abilities to recognize and prevent emerging threats.
2. Automated Incident Response: AI-powered systems can respond to cyber incidents in real-time and minimize the impact of attacks. Automated response mechanisms enable financial institutions to quickly isolate affected systems, mitigate damage, and prevent further breaches.
3. Fraud Detection: AI can analyze transaction data to identify suspicious activities and potential fraud. By recognizing patterns and anomalies, AI systems can flag fraudulent transactions for further investigation, which can help reduce financial losses.
4. Risk Assessment: AI-driven risk assessment tools can evaluate the vulnerability of financial systems and identify potential weaknesses. By proactively assessing risks, financial institutions can implement targeted security measures to protect their assets.
5. Behavioral Analysis: AI can monitor user behavior to detect unusual activities that may indicate a cyber threat. Behavioral analysis enhances overall security by identifying insider threats and unauthorized access attempts.

Challenges in implementing AI for cybersecurity

While AI offers significant benefits for cybersecurity, there are challenges involved in its implementation:

1. Data Privacy and Ethics: The use of AI in cybersecurity raises concerns about data privacy and ethical considerations. It’s imperative that financial institutions ensure AI systems comply with regulations and protect sensitive data.
2. Skill Shortages: There is a growing demand for professionals with expertise in AI and cybersecurity. Financial institutions should invest in training and development to build a workforce capable of implementing and managing AI-driven security solutions.
3. Integration with Legacy Systems: Integrating AI technologies with existing legacy systems can be complex and costly. Financial institutions need to carefully plan and execute integration strategies to maximize the benefits of AI.
4. Evolving Threat Landscape: Cyber threats are constantly evolving, so financial institutions have to stay ahead of new attack vectors. AI systems must be continuously updated and refined to address emerging threats effectively.

Essential skills for success in AI and cybersecurity

Professionals in the financial sector must develop a range of skills to succeed in the era of AI and cybersecurity:

1. Technical Expertise: A strong understanding of AI technologies, cybersecurity principles, and data analytics is essential. Professionals must be able to design, implement, and manage AI-driven security solutions.
2. Problem-Solving Skills: The ability to analyze complex problems and develop innovative solutions is crucial for addressing cybersecurity challenges. Employees must be able to think critically and adapt to changing threat landscapes.
3. Regulatory Knowledge: Understanding regulatory requirements and compliance standards is essential for implementing AI and cybersecurity measures. Staff must ensure that AI systems align with industry regulations and ethical guidelines.
4. Collaboration and Communication: Effective collaboration and communication skills are vital for working with cross-functional teams. Experts must be able to convey complex technical concepts to non-technical stakeholders and work collaboratively to achieve security objectives.

Conclusion

AI and cybersecurity are transforming the financial sector and presenting companies with significant opportunities and challenges. By leveraging AI technologies, financial institutions can enhance their cybersecurity measures, protect sensitive data, and maintain customer trust. However, the successful implementation of AI-driven security solutions requires a skilled workforce, strategic planning, and a commitment to continuous improvement. As the financial landscape continues to evolve, professionals with expertise in AI and cybersecurity will play a critical role in shaping the future of the industry.

About Software Mind

Software Mind is a global digital transformation partner with operations throughout Europe, the US and LATAM. For over 25 years they’ve been enriching organizations with the talent they need to boost scalability, drive dynamic growth and bring disruptive ideas to life. Top-notch engineering teams combine ownership with leading technologies, including cloud, AI, data science and embedded software to accelerate digital transformations and boost software delivery.

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