Raj Soni
CEO
Did you know that the banking sector has been one of the largest investors in AI, while the spending on generative AI is projected to reach $84.99 billion by 2030 with an astonishing 55.55% CAGR?
Wondering where to start or how to scale?
Read on to learn how you can put generative AI in banking to work to improve efficiency and customer experience.
Generative AI in banking refers to advanced, primarily large language models (LLMs) and related technologies that can:
Analyse vast amounts of financial and customer data,
Learn complex patterns, and
Autonomously generate new content, insights, and workflows tailored to your banking operations.
The key value that generative AI brings is that it can accelerate knowledge work, reduce human error, and free up your team to focus on higher-value tasks while improving accuracy, speed, and customer satisfaction.
Unlike traditional AI, which is typically limited to analysing structured data and making predictions based on predefined rules, generative AI can:
Process both structured and unstructured data,
Understand natural language, and
Produce contextually relevant outputs such as text, reports, or synthetic data.
For example, generative AI can produce natural-language responses in chatbots, create personalised financial plans, tasks that are difficult or impossible for conventional AI to handle.
Let’s explore the practical applications that highlight why generative AI is becoming indispensable in modern banking.
By harnessing the power of AI to analyse a customer's behaviour, transaction history, and unique preferences, you can now provide highly tailored recommendations and financial advice that feel truly individual.
This means no more generic, one-size-fits-all solutions. Instead, every customer experiences a financial journey that aligns with their needs and goals.
Imagine a customer who consistently saves in small amounts but is looking to increase their investment potential.
They might receive personalised tips on allocating their savings better, or even a recommendation to open an account that better fits their savings behaviour.
Beyond product offerings, AI’s ability to understand patterns allows for more proactive financial planning.
Therefore, it’s a great way to foster deeper, more meaningful customer connections.
For example, an AI system might predict when a customer is likely to need emergency funds, offering a tailored loan product with a lower interest rate before they even realise they need it.
As a result, customers feel understood, valued, and supported in their financial journey.
These personalised experiences can make all the difference in standing out and keeping customers engaged for the long run.
With Meniga’s Insights feature, you can create dynamic, context-driven content tailored to specific customer segments and avoid sending generic communication without considering individual needs and context.
Thus, you can segment customers by their unique spending habits, cash flow status, and financial goals, enabling you to reach the right customer, in the right context and at the right time.
Today’s AI-powered virtual agents, built on advanced LLMs, can understand context, sentiment, and intent.
Thus, they manage to handle complex, multi-step queries in real time, in text and voice interactions.
Instead of scrolling or tumbling through menus, your clients can now:
Instantly retrieve transaction details with explanations,
Launch and track a dispute case, and
Review the customer’s profile to generate a personalised balance transfer offer, with a breakdown of fees and interest savings.
Some of these chatbots are omnichannel-capable, meaning they can continue conversations seamlessly across mobile apps, websites, and even voice channels like smart speakers.
Thus, customers receive consistent support regardless of the platform they use.
Moreover, some speak in the customer’s preferred tone or language, adapting dynamically for accessibility or user preference for a more inclusive and user-friendly experience.
With generative AI, you can turn every portfolio into a highly personalised financial roadmap.
Imagine 2 completely different customer types:
A 35-year-old tech entrepreneur with a high-risk tolerance and interest in emerging markets, and
A retiring teacher seeking stability.
Would they seek the same financial things, or would you offer them the same ‘package’ or the type of service? Of course not. And this is where generative AI can help.
The tech entrepreneur might receive a portfolio recommendation that blends AI-selected tech startups, cryptocurrency assets, and ESG funds.
Meanwhile, the retiring teacher might receive a diversified income portfolio emphasising bonds, dividend-yielding stocks, and conservative REITs, optimised to preserve capital and generate steady returns.
Besides portfolio creation, generative AI continuously analyses live data, such as market trends, geopolitical events, and sector shifts to generate predictive financial models.
Thus, you quickly get insights that would otherwise take days or weeks to gather manually.
This agility improves performance, reduces risk exposure, and builds deeper client trust.
Moreover, generative AI can simulate different economic scenarios, such as:
Interest rate hikes,
Inflation spikes, or
Currency fluctuations, helping your clients understand how their portfolios would perform in various conditions.
As a result, they can make informed decisions with greater confidence, while you benefit from increased engagement, loyalty, and assets under management.
By analysing diverse data sources, generative AI provides a more holistic view of an applicant’s or portfolio’s creditworthiness, improving the precision of risk assessments.
In addition, by incorporating a wider range of data, including social, behavioural, and economic indicators, generative AI can create more sophisticated and inclusive credit scoring models.
This enables you to extend credit to previously underserved or overlooked customers while maintaining risk controls.
Furthermore, generative AI enables you to generate synthetic data for stress testing.
Let’s say your bank is developing a new credit scoring model for younger customers with limited credit history.
To test the model's reliability without using sensitive customer data, you use generative AI to produce anonymised synthetic datasets that mimic real-world behaviour, such as:
Spending patterns,
Repayment habits, and
Income fluctuations.
By ‘feeding’ this synthetic data into the model, you can safely simulate various economic conditions like interest rate hikes and job loss scenarios, and evaluate how the model performs.
All without exposing any real customer information. As a result, you can refine risk predictions and improve fairness before the model goes live.
Regulatory reporting traditionally requires countless hours of manual data gathering, validation, and formatting.
With generative AI, you can now automate the entire end-to-end reporting process, from data collection to document generation, ensuring accuracy, consistency, and timely submission.
Thus, you can automatically generate regulatory reports, audit trails, and compliance documentation required by authorities.
Generative AI systems continuously monitor transactions, communications, and operational processes to ensure adherence to regulations such as:
NIST,
PCI DSS,
DSP2, and
The Digital Operational Resilience Act.
These AI tools can:
Analyse structured and unstructured data,
Identify suspicious activities, anomalies, or potential breaches as they occur, and
Alert compliance teams in real time.
By automating these monitoring tasks, you reduce the risk of human error and ensure that compliance checks are thorough and up-to-date.
Furthermore, this proactive approach allows you to identify vulnerabilities before they escalate, strengthening overall compliance and security.
The regulatory landscape is prone to frequent updates and new requirements.
Generative AI can track regulatory changes in real time, automatically updating internal policies and compliance workflows to reflect the latest standards.
Therefore, you can adapt quickly, minimising the risk of falling behind on critical updates and reducing the manual burden on compliance teams.
Closely related to the above use, KYC and AML are essential to regulatory compliance in banking, thus deserving a separate mention.
Traditionally, analysts had to manually sift through documents like government-issued IDs, utility bills, tax filings, and bank statements to verify customer identities and assess risk.
With generative AI:
Optical Character Recognition (OCR) combined with LLMs can automatically extract structured and unstructured data from these documents.
The AI understands regional document formats, multiple languages, and even low-quality scans, significantly boosting onboarding speed and reliability.
Extracted data is instantly cross-referenced with internal systems and external databases like PEP and sanctions lists for real-time validation.
AML analysts often need to analyse long histories of complex financial activity. Generative AI helps by:
Summarising large volumes of transactions, flagging unusual patterns like layering, structuring, or rapid fund movement across borders.
Identifying behavioural risk indicators and generating alerts with contextual reasoning.
Producing concise risk summaries for onboarding, periodic reviews, etc.
Regarding detecting suspicious activities, Gen AI provides:
Drafting Suspicious Activity Reports (SARs) or internal AML narratives in clear, regulatory-compliant language.
Relevant facts, timelines, and justifications traced directly back to underlying data.
Standardisation across teams, improving auditability, and reducing legal exposure.
Today’s AI models go beyond basic thresholds and keyword triggers.
They’re trained on massive, multi-dimensional datasets, analysing millions of transactions in milliseconds to:
Detect unusual behaviour, such as a sudden high-value transfer from a new device in another country.
Spot subtle, evolving fraud tactics like micro-laundering or synthetic identity creation.
However, Gen AI doesn't just flag what’s obviously wrong, but it understands the patterns of what’s “normal” for each customer and detects nuanced deviations.
For example,
A user who always shops locally suddenly makes a purchase in Dubai at 3 AM — AI flags it.
A payroll account that usually transfers $5,000 weekly suddenly sends $50,000 to multiple new recipients — AI blocks it in real time.
By analysing factors like:
Location,
Device fingerprint,
Transaction velocity,
IP reputation, and
Historical behaviour, AI uncovers fraud schemes that would go unnoticed by human teams or static rules.
It’s only fair to mention potential challenges that Gen AI may bring.
Generative models thrive on data, but banks must balance innovation with privacy.
Using sensitive customer information, even to train anonymised models, can raise red flags under regulations like GDPR or CCPA.
Establishing clear data governance frameworks is crucial to avoid compliance pitfalls and maintain customer trust.
If the underlying data contains bias, the AI will likely replicate it, especially in credit, fraud risk, or onboarding decisions.
Without proper oversight, models may unintentionally exclude or disadvantage vulnerable customer segments, opening banks to reputational and regulatory risks.
Thus, you should regularly audit AI outputs for fairness, particularly in lending and fraud detection models.
| Top AI Governance Best Practices for Banks | |
| Best Practice | What It Means |
| 1. Clear Data Usage Policies |
|
| 2. Human-in-the-Loop Workflows |
|
| 3. Continuous Bias Monitoring |
|
| 4. Explainability Standards |
|
| 5. Cross-Team Alignment |
|
| 6. Safe, Phased Scaling |
|
Generative AI can “hallucinate,” producing convincing yet factually incorrect or legally risky content.
In banking, where precision is everything, you can’t overlook such an important aspect. Outputs used in customer communication, reporting, or compliance must be verifiable and traceable, requiring guardrails and human-in-the-loop review.
AI isn’t a plug-and-play solution. Many banks still struggle with legacy systems, siloed data, and skills gaps.
Scaling gen AI requires not just tools, but a cultural shift, training, and alignment across business, tech, and risk teams.
Despite these challenges, the upsides still far outweigh the risks, particularly when organisations take a responsible, phased approach.
Banks that succeed are those that combine strong AI governance with real-world use cases that deliver tangible value.
That’s why a forward-thinking digital banking solution like Meniga focuses on
Safe,
Transparent, and
Customer-centric applications, from personalised digital banking experiences to smarter financial insights, helping you innovate with confidence.
Meniga provides digital banking solutions to large financial institutions and banks to help modernise their online and mobile digital environments.
With Meniga, you can:
Instead, consolidate and enrich data from any source, including open banking.
Then, you can fully localise and customise data to get a 360-degree view of your customers’ spending habits and finances and provide tailored solutions and products.
As previously mentioned, AI is only as good as the data it is built on, which is why effective data consolidation, standardisation, and enrichment are crucial for any AI applications.
Our award-winning Engagement Platform allows you to go beyond generic notifications and deliver hyper-personalised campaigns in real-time. With the power of AI, you can:
Create ultra-specific segments for the ultimate personalised banking experience.
Analyse internal, open banking, and behavioural data for dynamic customer intelligence.
Use algorithms to adjust delivery based on preferences and real-time feedback.
Adopt predictive performance analytics to forecast performance to maximise impact
Our ML-powered Cashflow Assistant gives customers a clear, intuitive view of their past balance trends while forecasting future cash flow, empowering them to make smarter financial decisions.
AI-powered pattern detection identifies recurring transactions based on historical data, allowing users to play a what-if game, exploring how changes in income or expenses could affect their finances.
Beyond highlighting recurring income and expenses, it proactively alerts users to potential overdrafts or missed payments, helping them avoid unnecessary fees and stay in control of their finances.
Meniga makes saving simple and engaging by offering a suite of interactive savings modules that boost deposits and build healthy financial habits.
You can create personalised savings pots for specific goals like a new home, vacation, or rainy-day fund, and track their progress along the way.
With our Automated Savings, you can choose from pre-set or custom rules that automatically move money into savings based on:
Amounts,
Time intervals, or even
IFTTT-style triggers.
Each savings journey is supported by smart nudges and notifications, helping you stay motivated and in control of your goals.
Curious to find out more?
Contact us today and discover how to boost your banking strategy and customer loyalty in the long run.
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