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One of the ways banks are using AI is snooping on staff to prevent fraud

6 ways Kenyan banks are using AI  to serve customers | Pulselive Kenya

Denis Mwangi

The banking sector in Kenya is rapidly evolving, with Artificial Intelligence (AI) and Machine Learning (ML) at the forefront of this digital transformation.

According to the Central Bank of Kenya (CBK)'s 2023 Annual Supervision Report, local banks are increasingly leveraging these cutting-edge technologies to enhance efficiency, improve customer experience, and mitigate risks.

<h2>Ways Kenyan banks are using AI to usher new era of banking&nbsp;</h2>

<h3>Fraud Risk Management</h3>

One of the key areas where AI is making a significant impact is in fraud risk management.

Kenyan banks are deploying AI-driven solutions to monitor electronic communications among staff in trading rooms, detecting outliers and irregularities that could indicate fraudulent activities.

Additionally, banks are subscribing to fraud risk management solutions for payment cards provided by international card schemes and for account transactions.

These AI solutions generate a risk score that predicts the potential of fraud in authorisation attempts, helping banks to proactively prevent fraudulent transactions.

Cybersecurity is another critical area where AI and ML are playing a pivotal role.

Several Kenyan banks have adopted machine learning-powered solutions to <a data-link-role-code="open_new_tab" href="https://www.pulselive.co.ke/business/international/list-african-countries-most-vulnerable-to-cyber-threats-in-2023/dwpd890">detect both internal and external threats</a>.

Insider threats are monitored by analysing data on network usage patterns, work hours, and approved devices, while external threats are identified by spotting unusual activities in customer transaction patterns.

Moreover, AI-enabled Security Incident and Event Management (SIEM) tools automatically detect and respond to threats by quarantining suspicious processes, applications, and devices, thereby limiting the attack surface in case of a security breach.

<h3>Product Segmentation and Personalisation</h3>

AI and ML are also being used to enhance <a data-link-role-code="open_new_tab" href="https://www.pulselive.co.ke/business/domestic/new-report-on-naivas-quickmart-and-major-stores-delivers-insights-for-kenyan/qcw8gbk">product segmentation and personalisation</a> in the banking sector.

By grouping customers based on similarities, banks can offer tailored products and services that meet the specific needs of different customer segments.

According to the CBK report, machine learning techniques such as clustering and ensemble learning are being used to group similar data points and improve the performance of supervised learning tasks, leading to more personalised banking experiences for customers.

Customer service in Kenyan banks has been revolutionised by AI technologies.

Banks are now using AI-driven solutions to <a data-link-role-code="open_new_tab" href="https://www.pulselive.co.ke/bi/tech/6-social-media-analytics-tools-that-can-help-your-business/87z57jw">monitor customer sentiments on digital platforms, including social media</a>.

This feedback is summarised into major thematic concerns, allowing banks to gain deeper insights into customer needs and preferences.

Additionally, the introduction of Chatbots has streamlined digital banking, improving customer onboarding and facilitating transactions on internet and mobile banking platforms.

Businesswoman holding her laptop by the Nairobi city skyline

<h3>Anti-Money Laundering, Counter-Terrorism Financing &amp; Counter Proliferation Financing</h3>

AI is also transforming the way banks in Kenya approach <a data-link-role-code="open_new_tab" href="https://www.pulselive.co.ke/news/local/inside-governments-plan-to-track-accounts-of-politically-exposed-persons/yyesvhr">anti-money laundering</a> (AML), counter-terrorism financing (CTF), and counter-proliferation financing (CPF).

By using ML and Natural Language Processing (NLP), banks can improve customer screening processes, analysing past decisions, client files, watchlists, and public data to identify potential risks.

As these AI models learn over time, they provide more accurate results, aiding human analysts in making informed decisions.

<h3>Challenges and Opportunities</h3>

While the adoption of AI and ML in Kenya’s banking sector presents immense opportunities, it also comes with challenges.

Issues such as data privacy, algorithm bias, and the need for skilled talent are significant considerations that banks must address.

However, the potential benefits of AI could outweigh the risks, and with careful planning and execution, these challenges can be overcome.

As AI and ML technologies continue to advance, Kenyan banks are poised to introduce even more innovative applications in the coming years.

From customer service to fraud detection, these technologies are set to reshape the banking landscape, offering tailored financial products and services that meet the evolving needs of the Kenyan market.

6 ways Kenyan banks are using AI  to serve customers

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