The evolution of artificial intelligence (AI) in the financial sector has completely changed the way businesses deliver services. 

During the Techeconomy Business Series session titled “Major Lessons for Techies in Building Resilient, Customer-Centric Financial Solutions in an AI-Driven World”, experts shared their experiences and insights on how to innovate and maintain resilience while meeting customer needs.

Watch on YouTube:

Defining Resilience in Financial Solutions

Peter Kwakpovwe, founder of Draco Intelligence Ltd UK, broke down the concept of resilience in financial solutions. He stated, “Resiliency in financial solutions is the ability of the system to recover from shock, attack, or disruption, ensuring delivery and usage by customers.” 

He noted that resilience goes beyond simply fixing problems; it requires proactively creating systems that withstand disruptions.

Kwakpovwe shared a real-world example from his career: “We developed an AI-powered digital lending platform aimed at ease of banking, lending, and credit rating for customers. However, we faced challenges like political unrest and economic instability. To overcome these, we built an advanced anomaly detection algorithm to flag fraudulent transactions, an automated failover system, and a robust recovery and backup system.”

These measures ensured the platform could withstand disruptions and continue serving customers efficiently. According to him, “This reaffirmed my belief in AI-driven initiatives within the financial landscape.”

Ethical AI and Balancing Customer Needs

Ayodeji Ogunmola, director of Product Management at Northsnow Ltd UK, spoke on balancing rapid AI innovation with customer expectations. He noted, “The first thing customers want from financial institutions is to have their problems solved. AI brings simplicity to this process, but ethical considerations and transparency are important.”

Ogunmola highlighted two major issues:

1. “How ethical can the AI go?”
2. “Are customers trusting enough to allow AI integration into financial institutions?”

He explained the importance of defining what aspects of financial transactions are handled by AI versus humans. “We’ve seen human errors in resolving issues, but AI can bridge this gap, especially in interactions like chatbots that offer faster service.”

However, he also stressed the need for transparency: “Businesses must inform customers about what AI can and cannot do to build trust.”

Building Trust in AI: Transparency, Ethics, and Customer-Centricity are Key

On building trust in AI-driven financial systems, Moniade Adeniyi, product innovation and business growth strategist at Northsnow Limited, stressed the importance of transparency, fairness, and customer-centricity in AI solutions. 

He stated, “When you’re building an AI solution, it should be clearly explained how the AI system works. Customers need to feel safe and more confident about using the solution.”

Adeniyi also highlighted the need for ethical considerations, urging financial institutions to ensure that AI systems are unbiased. He said, “A financial institution must make sure that AI systems are fair and they don’t favour or harm any group of people.”

Personalised Financial Services

Ogunleye Oluwatobiloba, a data analyst with a fintech background, delved into how AI enhances customer experiences. “AI plays a huge role in delivering personalised financial services by leveraging data-driven insights,” he said.

He outlined key applications of AI:

• Customer Segmentation: “AI analyses customer data, such as transaction history and credit behaviour, to create detailed profiles.”
• Tailored Recommendations: “AI-powered engines suggest financial products like loans or investment options based on individual goals.”
• Chatbots and Virtual Assistants: “These tools, like UBA’s ‘Leo’, offer 24/7 support, handle transactions, and reduce the workload on human staff.”
• Fraud Detection and Prevention: “AI monitors user behaviour for anomalies, flags potential fraud, and notifies users immediately.”

Mitigating Risk with AI

The speakers collectively agreed that risk mitigation is an essential component of resilient fintech solutions. Kwakpovwe mentioned, “AI helps businesses holistically understand risk landscapes and develop de-risking strategies.”

Ogunmola added that iterative feedback and customer research are essential: “AI gathers data, analyses usage patterns, and helps businesses improve their offerings.” This aligns with the overarching goal of resilience—adapting to challenges while maintaining seamless customer experiences.

AI’s prospects in the financial sector are broad but require careful integration. Ogunleye said, “AI’s strength lies in its ability to personalise, automate, and innovate while addressing individual customer needs.” However, ethical considerations, transparency, and robust systems are essential to fostering trust and ensuring resilience.

The panel emphasised the importance of cross-disciplinary collaboration, with Kwakpovwe noting, “You need a strong, high-risk-skinned team to navigate the complexities of AI-driven solutions.”

Building Customer-Centric Financial Solutions Through AI

Peter Kwakpovwe emphasised the importance of leveraging AI to enhance financial literacy and inclusion. He highlighted the Central Bank of Nigeria’s (CBN) ongoing efforts to improve financial literacy through collaborations with banks and financial institutions. 

“By implementing this innovative admin solution, financial services can be customised for people who are particularly underserved and those who are in the underbanked population. But again, AI is data, data, and more data,” he said.

However, Kwakpovwe noted that while AI offers improved efficiency, fraud detection, and personalised customer services, the technology also introduces significant risks.

Adeniyi further noted the role of customer feedback in designing effective AI solutions, stating, “Every AI solution you are building should be tailored towards the customer… When you do all these things, trust is built, and they will want to use your system.”

The Dark Side: Data Privacy and Security Risks

Kwakpovwe did not mince words about the gravity of data privacy breaches, calling it the “data apocalypse.” He cited examples of data leaks in Nigeria, including breaches from major banks and the National Identity database. “The day you plug your product to AI, it automatically has access to everything you’ve had today—your data,” he warned.

To mitigate such risks, he recommended:

1. Robust Encryption: “If you build a database system and there are no strong encryptions, it’s a problem.”
2. Compliance with Data Protection Laws: He stressed the importance of adhering to regulations like GDPR and conducting regular audits.
3. Bias and Fairness Audits: Peter shared a Silicon Valley example where AI algorithms unintentionally discriminated against certain genders in loan approvals, showcasing the need for robust datasets and transparent algorithms.

Over-Reliance on AI: A Critical Pitfall

Kwakpovwe spoke on issues about the over-reliance on AI systems, which can lead to a reduced human thinking process. “Even the most intelligent people today are relying so much on AI,” he said. To address this, he suggested:

• Ensuring human oversight for key decisions
• Using AI as a support tool, not the primary driver of actions
• Investing in education and training to empower professionals with foundational skills

Technological Failures and Systemic Risks

Reflecting on a recent major banking system outage in the UK, Kwakpovwe noted the catastrophic impact of technological failures. “A core banking software issue shut down everything. It became a social media brouhaha,” he said.

To prevent such occurrences, Peter called for:

• Redundancy and Backups: “I’m a big fan of redundancy. You must have backups that have backups.”
• Incident Reporting Plans: Establishing clear protocols for managing AI system failures.

Scaling through Regulatory and Compliance Challenges

On regulatory hurdles, particularly in Nigeria and South Africa, Kwakpovwe stressed the importance of aligning AI systems with financial regulations to avoid fines and restrictions. “You must have regulatory engagement,” he said, adding that ethical AI frameworks and continuous monitoring are essential for compliance.

Adeniyi also stressed the importance of regular audits to maintain compliance and customer confidence, noting, “Regularly review the AI system to ensure they follow the law and meet high standards… This should be transparent to the customer.”

Leveraging AI to Drive Innovation in Fintech Solutions

Speaking on the importance of a systematic approach to deploying AI in financial services, Kwakpovwe said: “First off, there has to be this continuous learning of your data. AI can make decisions for you, but that shouldn’t be the final leg,” he noted. 

He called for hiring and training skilled professionals who can create and refine data models, stating, “You need over a billion scenarios… AI can help you create those scenarios, but you need someone to fine-tune and look at it also.”

Kwakpovwe emphasised the essence of closed user group testing before AI deployment. He explained: “Bring stakeholders into the room… go back to the product requirement document, tick all the boxes one by one… ensure your AI-driven product has achieved what you set out to do. When that’s done, move on to continuous improvement.”

He likened AI development to raising a child: “When you deploy it, AI starts learning on its own. It feeds on data, so regular audits and framework adjustment sessions are critical to ensure the system delivers sustainable value.”

Security and Scalability in AI Systems

The panellists highlighted the importance of cybersecurity in AI-driven systems. Pointing out the emergence of new roles such as large language model (LLM) cybersecurity experts: “These are people building systems to safeguard AI technologies. Such jobs didn’t exist five years ago, but they’re now crucial for protecting data and ensuring system integrity.”

Opportunities for AI in Africa

Addressing the future of AI in Africa, Ayodeji Ogunmola said: “There’s a lot of money in Africa that has not been harnessed yet. Voice-over AI could revolutionise financial inclusion by enabling people, especially those who aren’t tech-savvy, to access services through phone interactions. This can include account creation, KYC processes, and transactions.”

Ogunmola noted the benefits for underserved populations, such as rural farmers: “A farmer named Musa could get access to microloans because AI analyses his farming patterns and mobile data.”

AI’s Role in Fraud Detection and Improved Customer Experience

Fraud detection is an important subject when it comes to AI. Ogunleye Oluwatobiloba expatiated this: “AI can help in fraud detection and prevention by monitoring transaction patterns and reducing fraud rates, building customer trust.”

He noted additional benefits of AI, including:

• Smart payment gateways for instant, secure cross-border transactions.
• Dynamic currency conversion providing real-time rates.
• AI-driven credit scoring systems enabling microloans for customers based on their transaction histories.

AI and Human Expertise: A Synergistic Future

Countering fears of job displacement by AI, Moniade Adeniyi reassured professionals: “AI does not have a mind of its own; it resonates based on the information you provide. Professionals must train AI to respond effectively to human needs. Instead of losing jobs, we’ll be creating scenarios and guiding AI’s learning process.”

Adeniyi noted that this approach would open opportunities across sectors, urging professionals to stay proactive in adapting to the AI era.

Organisations must embrace AI responsibly, ensuring human oversight and continuous improvement. “If you follow these guidelines, you’ll create a system that works as it should—delivering value to both the organisation and its customers in a sustainable way.” This will drive innovation, inclusion, and resilience across the financial sector.

Listen on Spotify.

Gone are the days when product decisions were based purely on intuition or anecdotal evidence. Today, companies—especially those in 1ntech, OEMs, and tech product sales—must leverage data as a strategic asset. Data is no longer just an output that reRects what happened; it is a core input that drives every product decision, iteration, and innovation.

For tech enthusiasts and professionals striving to make a mark in product management and development, understanding how to build smarter products with advanced analytics is crucial.

Let’s explore how data engineering and advanced analytics converge with product development, the importance of data as a strategic input, and how companies can leverage this power to stay ahead.

Data as the Foundation of Modern Product Development

Traditionally, product development followed a linear path: de1ne the problem, design a solution, build the product, and test it.

While this approach worked in the past, it often left companies playing catch-up, launching products that were outdated by the time they hit the market or that failed to meet evolving customer needs.

Data serves as the backbone of the product development lifecycle. Companies are continuously collecting data from multiple sources: customer behaviour, usage analytics, feedback forms, A/B tests, social media interactions, and IoT sensors.

This data provides insights into how products are used, what features are most valuable, and where there are pain points or opportunities for improvement.

By integrating advanced analytics and machine learning (ML) into product development, businesses can move from reactive to proactive strategies—predicting what customers want and need before they even ask.

Imagine a 1ntech company rolling out a new budgeting app. In the past, the product team might have developed features based on focus group feedback or market research. But in a data-driven approach, the development process starts with analyzing usage data from millions of customers—how they interact with existing budgeting tools, what they struggle with, and which features they prefer. This approach not only informs the development of the most valuable features but also identi1es the ones that should be deprioritized. With data as the foundation, every decision becomes a calculated move, increasing the chances of building a successful product.

Convergence of Data Engineering and Product Development

To fully utilize the power of data, companies need more than just raw information—they need a robust data engineering architecture capable of collecting, processing, and analyzing massive datasets in real time. This is where data engineers, data scientists, and product managers work together to build pipelines and models that transform data into valuable insights.

The convergence of data engineering and product development means building products that are not only data-informed but also data-empowered. Here’s how these disciplines intersect:

a.  Data Infrastructure Development

The foundation of a data-driven product strategy begins with building the right infrastructure. This involves setting up data lakes, integrating APIs, and connecting various systems to capture real-time customer interactions.

For OEMs and sales businesses, this may include linking CRM systems with e-commerce platforms, sensors in hardware devices, and even third-party data sources to create a comprehensive view of the customer’s journey.

b.   Advanced Analytics for Actionable Insights

Once the infrastructure is in place, data scientists and product managers collaborate to design advanced analytics models.

These models use machine learning algorithms to identify patterns, predict customer behaviour, and optimize product features.

For example, in tech product sales, ML algorithms can analyze customer purchase behaviour to predict which products are most likely to sell in certain regions, allowing sales teams to adjust strategies dynamically.

c.   Iterative Product Development Based on Data Feedback Loops

One of the most signi1cant advantages of a data-driven approach is the ability to iterate quickly. Continuous monitoring and analysis create feedback loops where products are tested, analyzed, and re1ned based on real-time customer interactions. For example, A/B testing can be used to measure the electiveness of a new feature, while behavioural analytics can pinpoint exactly where users drop oI in a product’s funnel, allowing the development team to optimize the user experience.

A Case Study

Let’s take a closer look at how data-driven product development unfolds in a real-world scenario:

A global OEM company is developing a new line of smartwatches aimed at tech-savvy users who want a comprehensive health-monitoring device. The product team initially designs the device based on industry research and consumer feedback. However, instead of launching the product with this basic set of features, they implement a data-driven approach.

Step 1: Early Data Collection

The company rolls out a beta version of the smartwatch to a select group of users. Data engineers build a system to collect anonymized usage data, such as which health metrics are most frequently viewed, the time users spend in each app, and which settings are customized the most.

Step 2: Advanced Analytics and Iteration

With this data Rowing in, data scientists analyze it to identify patterns. They 1nd that users are highly engaged with the sleep-tracking feature but often ignore the hydration reminders. Additionally, customers in Asia frequently use the stress-monitoring feature, while those in Europe prefer the exercise tracker. Based on these insights, the product team decides to enhance the sleep-tracking algorithm and localize features to cater to diIerent regions’ preferences.

Step 3: Predictive Models for Future Releases

The data team then builds predictive models to anticipate future needs. For instance, they develop an algorithm that predicts when a user might be stressed based on their activity patterns and environmental factors (e.g., time of day, location). The product team integrates this feature into the smartwatch, making it not just a passive tracker but an active assistant that oIers personalized suggestions, like meditation exercises when stress levels are high.

By embedding data and analytics into every phase of development, the OEM company builds a product that continuously evolves based on real-world user data. This approach doesn’t just improve customer satisfaction; it ensures that the product remains relevant and valuable over time.

Data-Driven Decision Making: Moving from Metrics to Actionable Insights

Collecting data is one thing; using it eIectively is another. The biggest challenge many companies face is turning data into actionable insights that drive real change. This is where data-driven decision-making becomes critical:

a.  Defining Key Metrics

To measure the success of a product, businesses need to de1ne the right metrics, such as user engagement, customer satisfaction scores, retention rates, and conversion rates. However, these metrics must be aligned with business goals.

For instance, if the goal is to increase product stickiness, focusing on metrics like daily active users (DAUs) and feature engagement levels will provide more meaningful insights than revenue alone.

b.   Real-Time Monitoring and Alerts

Advanced analytics tools enable real-time monitoring of these metrics, allowing product teams to react quickly when deviations occur.

For example, if a sudden drop in user engagement is detected, the team can drill down into the data to understand the cause—be it a technical issue, a poorly performing feature, or a mismatch between customer expectations and product delivery.

c.   Actionable Insights Through Predictive Analytics

Using ML, companies can go beyond descriptive analytics (what happened) and diagnostic analytics (why it happened) to predictive analytics (what will happen next). In 1ntech, for instance, predictive models can identify customers who might default on loans or highlight the best times to oIer promotions based on past transaction patterns.

Building a Culture of Data-Driven Innovation 

Data scientists, engineers, and product managers need to work together seamlessly. By breaking down silos, they can ensure that insights Row freely and are translated into product improvements e ciently.

The tech landscape is ever changing, and so are customer needs. Companies must adopt a growth mindset, continuously experimenting with new features, testing hypotheses, and learning from the outcomes. This agility ensures that they stay ahead of competitors and respond proactively to changes in the market.

Empowering product teams with the right tools and access to data helps them make informed decisions faster. Self-service analytics platforms allow product managers to pull reports, explore trends, and test diIerent scenarios without relying heavily on data teams.

The Future: Integrating AI and IoT for Next-Level Product Development

The future of product development is not just data-driven; it’s AI-powered. AI models that learn and evolve autonomously will take product development to the next level. Here’s a glimpse into what’s next:

• IoT Integration: IoT devices will provide real-time data on how products are used in various environments, oIering insights that can be used to develop more intuitive and adaptive
• AI for Proactive Product Iteration: AI algorithms will identify trends and patterns faster than any human team, recommending or even automating product updates before issues
• Blockchain for Transparent Data Utilization: As data privacy becomes a growing concern, blockchain will play a pivotal role in ensuring that data is used ethically, with full transparency and customer

Conclusion

In a world where data is abundant, the challenge isn’t acquiring information; it’s extracting value from it. Understanding and implementing a data-driven product development approach is no longer optional. It’s the foundation for building smarter, more innovative products that resonate with customers, adapt to market demands, and oIer personalized experiences. By leveraging advanced analytics, machine learning, and AI-driven insights, businesses can turn raw data into actionable intelligence—fueling product decisions, enhancing user experiences, and gaining a competitive edge.

Who is Peter Kwakpovwe?

Peter Kwakpovwe is a distinguished Data Scientist and business leader based in the UK. As a certified Scrum Product Owner (CSPO) and a champion of data transformation, he has a proven track record of leading successful business transformations through the strategic application of data, finance, and technology.

With over 12 years of experience in various managerial roles, Peter has been instrumental in building digital products and deriving actionable data insights within the Fintech sector and other digital enterprises. His notable achievements span revenue growth, operational efficiency, business development, and product management, earning him numerous awards and recognition in digital media.

Peter’s expertise encompasses product requirement elicitation, business process re-engineering, data analysis, change management, and the development of digital adoption roadmaps.

He is particularly passionate about creating machine learning models that optimize operations, developing impactful digital products that enhance customer engagement, and extracting meaningful insights from data to drive strategic planning and development.

[Featured Image Credit]