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LEVERAGING AI AND ANALYTICS IN RETAIL BANKING


Profile SoftwareInsightsArticlesLeveraging AI and Analytics in Retail Banking
18.09.2024 Articles

The rapid digital transformation in retail banking has placed artificial
intelligence (AI) and advanced data analytics at the forefront of the sector’s
future. As customer expectations shift toward more personalised, seamless
experiences, banks should consider investing in AI and data-driven solutions to
remain competitive.

This article explores how retail banks can leverage AI and analytics for
operational efficiency, enhanced customer experience, fraud prevention, and
more. The aim is to help decision-makers in retail banks understand the
transformative impact of AI and how to successfully integrate it into their
strategies.

 


THE ROLE OF AI AND ANALYTICS IN BANKING 

AI and analytics are revolutionising every aspect of the banking industry,
particularly in retail banking, where customer-centric strategies drive
significant results. According to Accenture, AI will boost productivity by
22-30%, while revenue could be increased by 6%. Thus, the adoption of AI crucial
for banks seeking a competitive edge.

With the ability to process vast amounts of data, AI offers retail banks the
tools to gain deeper customer insights, streamline operations, and mitigate
risks.

 


THE BUSINESS CASE FOR AI IN RETAIL BANKING 

As competition intensifies, retail banks need to streamline operations, improve
customer experience, and enhance decision-making. This section explores why AI
is essential for achieving these goals and creating a sustainable competitive
advantage.


INCREASED EFFICIENCY AND COST REDUCTION 

AI enables banks to automate repetitive tasks such as document verification,
loan approvals, and customer inquiries. According to industry estimations, AI
could deliver up $200 billion and $340 billion in value annually to the global
banking industry, primarily through operational efficiencies. Gen AI, through
AI-powered chatbots and virtual assistants provide 24/7 customer service,
handling routine inquiries while freeing up human resources for more complex
tasks.


ENHANCED CUSTOMER EXPERIENCE 

Personalised services are now a baseline expectation for banking customers. AI
can help banks deliver on these expectations by using predictive analytics to
recommend tailored products and services. For instance, AI can analyse customer
spending patterns to offer targeted financial advice, resulting in a more
engaging and relevant customer experience.


RISK MANAGEMENT AND FRAUD DETECTION 

In an era of growing cyber threats, fraud detection and risk management have
become critical. AI’s machine learning algorithms can quickly identify
suspicious patterns and transactions, flagging them for further investigation.

 



 


KEY AREAS WHERE AI AND ANALYTICS ARE TRANSFORMING RETAIL BANKING

AI is reshaping the retail banking landscape, driving innovation across customer
service, fraud detection, and operational efficiency. In this section, we will
delve into the specific areas where AI and advanced analytics are making the
biggest impact, helping banks stay ahead of the curve.


CUSTOMER INSIGHTS AND PERSONALISATION

AI offers unparalleled insights into customer behavior. By analysing customer
data, banks can provide personalised services such as customised loan offers or
savings plans. For instance, AI can predict which customers are likely to apply
for a mortgage based on their financial history and browsing patterns. This
level of personalisation improves customer satisfaction and fosters long-term
loyalty. 


AUTOMATION OF ROUTINE BANKING PROCESSES

AI is redefining banking workflows by automating routine tasks, allowing human
staff to focus on higher-value activities. Digital customer onboarding, for
example, is now being streamlined using AI-driven KYC (Know Your Customer)
processes. Instead of manually verifying documents, AI systems can extract and
validate customer information instantly, reducing onboarding time from days to
minutes. 


OPTIMISING LENDING AND CREDIT DECISIONS

Lending is one of the areas where AI has the most significant impact.
Traditional credit scoring models rely on limited data points, while AI can
analyse vast amounts of data in real-time to assess credit risk. This allows
banks to make more informed and faster lending decisions. AI-powered tools can
also monitor the financial health of borrowers, predicting defaults before they
happen.

 


BEST PRACTICES FOR AI IMPLEMENTATION IN RETAIL BANKING

Successfully adopting AI in banking requires more than just technology—it
demands strategic planning, quality data, and collaboration across teams. This
section outlines essential best practices for retail banks looking to implement
AI effectively, ensuring smooth integration and long-term success.


INVEST IN DATA QUALITY AND INFRASTRUCTURE

The foundation of successful AI implementation is high-quality data. Banks must
ensure that their data is clean, structured, and stored in systems that AI can
easily access. Investing in a robust data infrastructure is critical for
achieving accurate and actionable insights from AI tools.


COLLABORATION BETWEEN AI EXPERTS AND BANKING TEAMS

AI implementation should not be a siloed effort. Collaboration between AI
specialists and retail banking professionals is key to ensuring that AI models
are tailored to specific banking challenges. Cross-functional teams can help
bridge the gap between technical and business requirements, driving more
successful outcomes.


ENSURING COMPLIANCE AND SECURITY

With the increasing reliance on customer data, privacy and security are top
concerns. Banks must ensure that their AI implementations comply with
regulations such as GDPR and the CCPA. This involves using secure encryption
methods, anonymising customer data, and building systems with privacy-by-design
principles.

 


CHALLENGES AND CONSIDERATIONS FOR RETAIL BANKS USING AI

While AI offers immense potential, there are also hurdles to navigate. Retail
banks must address issues like data privacy, security, and the high cost of AI
implementation. This section discusses the key challenges banks face and what
they should consider when deploying AI solutions.


COST OF AI IMPLEMENTATION

Implementing AI may come with upfront costs, including purchasing technology,
integrating it with existing legacy banking systems, and training staff.
However, selecting a dedicated AI system, offering tailored support and
training, continuous innovation and seamless integration with various BI/DW
subsystems, provides significant benefits in terms of cost saving, increased
efficiency, and improved customer experience. 


CHANGE MANAGEMENT

Resistance to change is a common challenge when implementing new technologies.
Banks must invest in training programs to help staff adapt to AI-powered
systems. Change management strategies should focus on demonstrating the benefits
of AI to employees and integrating AI seamlessly into existing workflows. 

 


THE FUTURE OF AI AND ANALYTICS IN RETAIL BANKING

The future of retail banking is undeniably AI-driven. As AI technologies
continue to evolve, banks will see new opportunities for predictive services,
personalised banking, and enhanced customer engagement. 


GROWING ROLE OF AI IN PREDICTIVE BANKING

Predictive analytics is the future of customer engagement in retail banking. AI
will allow banks to anticipate customer needs and offer personalised financial
products proactively. For instance, AI could notify a customer when it’s an
ideal time to refinance their mortgage or invest in a new savings plan based on
their financial goals. 


AI-POWERED FINANCIAL ADVICE

Robo-advisors are becoming increasingly popular for providing automated,
AI-driven financial advice. By analysing a customer’s risk tolerance and
financial goals, AI can recommend personalised investment strategies, making
financial planning accessible to a broader audience. 

 


RIDE THE AI WAVE

AI and analytics are no longer optional; they are essential tools for any retail
bank looking to stay competitive in today’s market. By enhancing efficiency,
personalising customer experiences, and improving risk management, AI offers
banks the way to thrive in the digital age.

However, banks must take proactive steps to integrate AI into their operations.
The key is to start small with manageable projects but scale quickly to realize
the full potential of AI. A well-planned AI strategy can help retail banks
reduce costs, improve customer satisfaction, and stay ahead of their
competitors.

Finuevo Suite is designed to support the digital transformation journey of
retail banks by integrating advanced AI technologies into their core and digital
banking operations. With its innovative, integrated AI solution, banks can
automate critical processes like digital customer onboarding, KYC compliance,
and real-time fraud detection, while its powerful core engine leverages data
analytics to offer personalised customer experience and optimise lending
decisions.

 

References:

Banking on AI – Banking Top 10 Trends for 2024 [Accenture]

Scaling gen AI in banking: Choosing the best operating model [McKinsey &
Company]

Artificial Intelligence & its role in the fight against fraud [PwC]

How Artificial Intelligence is Transforming the Financial Services Industry
[Deloitte]

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