Customers expect financial management to be automatic, intuitive, and personal.
However, most banking apps stop at charts and alerts, leaving customers to find out what to do next on their own.
Consequently, when customers don’t get real help, they turn to digital-first competitors who offer AI-driven financial assistants, predictive budgeting, and goal-based automation.
Read on to learn how next-gen Personal Finance Management (PFM) can help you deliver personalised insights, automated savings, and conversational support that strengthen loyalty and open new revenue opportunities.
What is next-gen PFM: Overview and key features?
Next-generation Personal Finance Management (PFM) represents a new era of digital financial tools, powered by AI, machine learning, and advanced analytics.
These platforms go far beyond traditional budgeting or simple expense tracking.
They offer deeply personalised, proactive, and holistic financial experiences, combining real-time data and predictive insights.
The goal is to make financial management seamless, intuitive, and truly human-centric.
How? It helps customers monitor their money, as well as plan, act, and achieve their financial goals with confidence.
Defining features of next-gen PFM
Bank leaders recognise that launching a simple PFM module is no longer sufficient; most apps appear similar and struggle to deliver measurable results. What differentiates next-gen PFM is its ability to go beyond reporting and become a driver of engagement, deposits, and cross-selling.
Below are the seven capabilities every bank should prioritise.
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AI-driven personalisation: Advanced AI creates highly tailored financial advice, product recommendations, and automation based on an individual's spending habits, income, goals, and behavioural patterns.
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Hyper-personalised insights: Customers receive dynamic, contextually relevant alerts and suggestions for saving, investing, debt management, and tax optimisation that adapt over time.
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Conversational interfaces: Natural language processing enables banking customers to interact with their finances conversationally across messaging apps, voice assistants, and banking apps, making finance more accessible and engaging.
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Automated, goal-based savings: AI automatically adjusts savings plans based on real-time cash flow analysis to help users effortlessly meet their financial targets.
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Integrated financial wellness: Next-gen PFM blends financial management with personal well-being, recognising the emotional and cognitive aspects of money management, thus reducing stress and improving confidence.
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Omnichannel and seamless experience: Customers interact and receive financial insights consistently across multiple devices and platforms, from smartphones to wearables.
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Focus on financial education and inclusion: Next-gen platforms often include educational modules and extend services to underserved populations by leveraging alternative credit scoring and AI-powered financial planning.
How does next-gen PFM differ from traditional product-market fit?
Traditional product-market fit is about finding and maintaining product-market alignment for growth and sustainability, often focused on broad market needs.
On the other hand, next-gen PFM redefines this concept by focusing on hyper-personalised, AI-enhanced financial experiences that continuously evolve with individual customers’ changing needs and behaviours.
| Aspect | Traditional product-market fit | Next-gen personal finance management (PFM) |
| What it measures | Market demand fit for a product | Personalised financial health and management |
| Approach | Target market segment alignment | Individualised, AI-driven hyper-personalisation |
| Nature | Stable and repeatable product-market alignment | Dynamic, continuously adapting based on user behaviour |
| Key technology | Minimal or generic tech | Advanced AI, machine learning, conversational interfaces |
| End-user experience | Functional, solves a market need | Proactive, emotionally intelligent, seamless across channels |
| Outcome | Product growth and scale | Holistic financial wellness and behaviour optimisation |
| Time horizon | Achieved at a point and sustained | Ongoing with continuous improvement and evolution |
Why do banks need next-gen PFM?
Next-gen PFM platforms anticipate customer needs, provide actionable insights, and even automate routine tasks such as savings or bill payments.
The result is a deeper, more meaningful relationship with customers, built on trust, convenience, and value.
Additionally, they offer banks a strategic advantage by enabling:
Customers are more likely to adopt additional products, such as loans, credit cards, or investment services, when recommendations are timely, relevant, and personalised. Furthermore, proactive insights help customers avoid overdrafts, manage debt, and reach financial goals, reinforcing the bank as a trusted partner rather than just a service provider.
4 key features of next-gen PFM you should know about
We've already touched on the key characteristics of next-gen PFM, but now let’s take a closer look at the top features driving this transformation.
1. Conversational banking: What does it do?
Conversational banking is AI-first, predictive, and contextually aware.
It goes far beyond basic FAQs or static chatbots, acting as a personal financial assistant that can understand, advise, and execute.
Powered by Large Language Models (LLMs) fine-tuned for banking, integrated with real-time transaction data, and connected to action layers, it transforms how customers interact with their finances.
Key capabilities include:
Natural-language interactions
Conversational banking now allows users to communicate naturally, without menus or rigid commands:
1. Plain-language queries:
2. Multi-turn conversations:
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'Move $500 from my checking to savings.'
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'Actually, make that $700 and set a recurring transfer every Friday.'
3. Persistent context
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The assistant remembers ongoing goals, previous questions, and preferences across sessions, providing continuity and reducing repetitive inputs.
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For example, a user setting a 'guilty pleasure fund' today will find the assistant tracking progress and offering actionable suggestions weeks later.
4. Predictive prompts
The AI can anticipate questions or needs based on transaction patterns, such as reminding a user about upcoming bills, suggesting budget adjustments, or highlighting unusual spending trends.
Multimodal capabilities
Modern PFM assistants aren’t limited to text. They support multiple input and output modes to make interactions intuitive and inclusive.
1. Voice-enabled banking
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Users can speak to their assistant through mobile apps, smart speakers, or even connected vehicles.
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This supports hands-free transactions, reminders, and financial insights.
2. Image recognition
Users can upload receipts, invoices, or bills. The assistant can:
3. Document parsing and analysis
Beyond receipts, assistants can process mortgage statements, investment summaries, or insurance policies to provide tailored insights and recommendations.
4. Hybrid human-AI support
When issues are complex, the system can seamlessly transfer the session to a human advisor while providing full context, avoiding repetition, and improving resolution speed.
Embedded actions
One of the most impactful innovations is integrated action within the conversational interface.
Customers can ask questions and act immediately on insights:
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One-tap execution: Pay bills, transfer funds, block or replace cards, or dispute transactions, all without leaving the chat or voice interface.
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Contextual recommendations: For example, after noting an upcoming bill, the assistant might prompt: 'You have $200 left in discretionary spending this week. Should I move $100 to cover your electricity bill?'
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Seamless cross-channel experience: Whether on mobile, web, or voice, actions taken in one channel are reflected across all others, ensuring continuity and accuracy.
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Proactive automation: The assistant can also automate repetitive tasks, such as recurring transfers or micro-savings, based on customer consent and preferences, reducing cognitive load while keeping customers in control.
Trends that are shaping 2025
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Proactive conversations: ‘I noticed your electricity bill increased by 15% this month. Should I help set a budget adjustment?’
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Multilingual and multicultural support: Real-time translation for global user bases.
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Conversational credit applications: Users apply for loans or cards inside chat, with instant eligibility decisions explained clearly.
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Emotion detection: Voice tone and text sentiment analysis for better escalation handling.
How Meniga reshapes customer engagement
Meniga is set to redefine customer engagement for banks with the Conversational Financial Assistant, an advanced, LLM-powered solution designed exclusively for banks.
Customers can ask everyday questions, such as ‘How much did I spend on groceries this month?’ or ‘Can I afford a weekend getaway?’ and receive instant, accurate answers in a natural, conversational flow.
By leveraging enriched data and operating with banking-grade security, this intelligent assistant provides:
As a result, you can position your bank as a forward-thinking, trusted financial partner.
2. Automated savings and liquidity management
AI-driven automated savings tools analyse users’ income, cash flow, and spending habits to proactively and consistently set aside money toward financial goals such as emergency funds, retirement, or purchases.
Some of the techniques include:
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Intelligent sweep rules: Sweeps extra funds from checking to savings only when safe to do so, factoring in upcoming bills, income cycles, and predicted discretionary spending.
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Goal-based automation: Customers can set goals, for example, $5,000 emergency fund in 6 months, and the AI determines daily or weekly contribution amounts, adjusting in real time as circumstances change.
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Smart liquidity controls: Automated prevention of overdrafts by reverse sweeping funds from savings or linked accounts when low balances are predicted.
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Micro-savings and trigger-based saving: Rounds up transactions or saves small amounts when spending less than usual in a category. For example, you spent $20 less on bills this week, moving that to savings.
Trends that are shaping 2025
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AI-powered cashflow forecasting: Predictive models detect shortfalls weeks in advance and adjust savings plans automatically.
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‘Set and Forget’ Saving-as-a-Service: Users can opt for fully managed savings without needing to configure rules manually.
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Connected goals across life events: If a user books a trip, the AI auto-creates a travel savings plan.
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Integration with Investments: Surplus funds automatically move into low-risk investments once emergency fund goals are met.
How can you help your customers grow deposits with Meniga?
Meniga’s Smart Savings solution enables your customers to create savings pockets to achieve personal goals, such as vacations, cars, or home deposits.
They can choose pre-existing or build their own savings rules, which automatically transfer money into savings at specific amounts or intervals, or are tied to IFTTT events.
All Meniga Savings modules include out-of-the-box nudges and notifications to help customers track and monitor their savings progress:
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Round up transactions to the nearest €
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Tax-specific categories, such as guilty pleasures
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Save a percentage of your income, and more.
Thanks to these gamified and automated savings rules based on personalised insights, your customers will increase their savings much more efficiently.
3. Personalised financial insights and recommendations
AI and machine learning convert transactional and behavioural data into dynamic, actionable insights tailored to each individual’s financial situation.
Thus, instead of generic nudges, the system provides actionable, trust-driven insights:
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‘You spent 30% more on dining this month compared to your 3-month average. Want to adjust your dining budget or set a savings challenge?’
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‘Your credit card APR is 4% higher than your risk profile suggests. Would you like me to find alternatives?’
Insights can vary from behavioural spending analysis, cash flow predictions, goal progress, and optimisations to product and offer recommendations.
Trends that are shaping 2025
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Explainability first: Recommendations include a clear ‘Why’ statement. For example, ‘We recommend consolidating because you’ll save $180 in interest over 6 months’.
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Hyper-personalisation: Based on life stage, spending patterns, and psychographic data.
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Multichannel delivery: Insights appear in feeds, push notifications, and conversational interfaces.
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AI-driven ‘Next Best Action’: Prioritises top actions for financial health, such as paying down high-interest debt before investing.
How can you drive loyalty with Meniga’s personalised Insights?
Our award-winning AI-powered Insights solution enables you to create hyper-personalised insights that are triggered or scheduled by a transaction, and backed by rich data enrichment.
It allows you to analyse behavioural data to create ultra-specific customer segments, such as Likely to Invest or Starting a Family, to ensure tailored and timely delivery.
Thus, you can notify a customer:
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About the late deposit,
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Doubled recurring expenses
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About the remaining budget
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How much money is left to spend based on the end-of-the-month criterion
Additionally, you can create your own insights and use dynamic values to personalise the message and deliver it to any customer-facing channel or your own system.
As a result, you can send the right message to the right person in the right context in real-time.
4. AI financial assistants
Advanced AI financial assistants integrate conversational AI, predictive analytics, and goal-based financial planning to provide personalised coaching and support.
Core features include expense tracking, budgeting, goal setting with progress monitoring, and smart investment advice powered by Robo-advisors with real-time market analysis.
Here are some examples of how it can look in practice:
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Proactive financial coaching: Alerts for irregularities. For example, ‘Your subscription costs increased by $25 this month. Should I cancel unused services?’
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Dynamic budgeting: Adjusts category budgets automatically based on real-time income and expense changes.
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Goal management: Suggests micro-goals, such as ‘Save an extra $50/week for 6 weeks to hit your vacation goal early'.
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Automated execution: Moves money, sets up recurring transfers, and even books deposits into high-yield accounts.
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Tax and compliance awareness: Prepares tax-friendly investment recommendations or highlights deductions.
Trends that are shaping 2025
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Voice-activated full-service assistants: ‘Hey [Bank Name], increase my emergency fund goal by $500 and find me a lower-interest card.’
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Predictive goal planning: Anticipates future needs. For example, ‘You’re likely to need $2,000 for annual insurance next month—start saving now?'
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Hybrid advisory model: Escalates complex scenarios to human advisors seamlessly with full context.
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AI negotiation capabilities (future trend): Assistants can negotiate bills or subscription discounts with vendors on behalf of the user.
What are the key AI enablers driving next-gen PFM?
Delivering hyper-personalised, proactive financial experiences at scale isn’t possible without a robust AI foundation. Here are the core enablers that make it work:
1. LLM and banking terminology
At the heart of conversational banking and intelligent financial assistants are Large Language Models (LLMs) fine-tuned with banking-specific terminology.
This ensures the system accurately understands industry terms, regulatory constraints, and financial behaviours.
Unlike generic AI, these domain-trained models can interpret nuanced queries such as:
‘Can you schedule an extra payment on my mortgage for next month and adjust my savings goal accordingly?’
As a result, conversations feel natural, compliant, and context-aware, minimising the risk of misinformation.
2. Contextual memory
One of the most significant leaps is the development of persistent, secure contextual memory. Instead of starting from scratch every session, the system remembers user preferences, past interactions, and ongoing goals.
For example, if a customer previously asked about ‘vacation savings,’ the assistant can follow up later with:
‘You’re 80% towards your vacation goal. Should I accelerate contributions using last month’s surplus?’
3. Action layer integration
Insights without execution are meaningless.
That’s why modern PFMs integrate an action layer that connects AI-driven recommendations directly to core banking systems, payment rails, and third-party APIs. This enables real-time actions like:
The customer can complete these actions instantly within the conversation, eliminating friction and reinforcing the sense of a true digital financial assistant.
4. Real-time data streaming
Next-Gen PFM thrives on instant, accurate, and actionable insights.
Real-time data streaming allows banks to continuously process transactions, account activity, market fluctuations, and external financial events as they happen.
As a result, customers can get:
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Immediate notifications for unusual transactions or upcoming bills.
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Dynamic budget adjustments that reflect real spending patterns.
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Predictive recommendations that adapt in real time to changes in income or expenses.
By integrating streaming analytics with AI models, banks can transition from lagging insights to proactive guidance, providing customers with a sense of control and confidence in every financial decision.
5. Explainability frameworks
As AI takes on more decision-making, transparency and trust become a must.
Explainability frameworks provide clear, user-friendly explanations for AI-driven recommendations and actions.
Features include:
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Visual dashboards showing why a suggestion was made.
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Breakdown of the data points and predictive models influencing decisions.
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Users have options to adjust their preferences or provide feedback to fine-tune the system.
These frameworks satisfy regulatory requirements and also reinforce user trust, making customers more likely to follow AI-driven advice for savings, investments, or credit management.
How can you make your bank future-ready with Meniga’s next-gen PFM?
Next-Gen PFM, powered by AI, conversational interfaces, and predictive insights, empowers banks to anticipate customer needs, simplify decision-making, and build trust.
Meniga offers forward-looking PFM and hyper-personalised solutions for banks that want to embrace transformation.
We help financial institutions position themselves as innovative and customer-centric partners.
AI is only as good as the data which feeds it.
If you feed it fragmented or inconsistent data, the output will be incomplete or worse, completely wrong.
Meniga consolidates and enriches raw transaction data from any source, including open banking, building the foundation for AI-powered innovation.
Our Enrichment Engine delivers cleaner categorisation, smarter analytics, and continuous learning.
As a result, you can:
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Provide a clear and accessible overview of each transaction, including category, merchant details, and spending history, to help your customers understand their finances and create healthy financial habits for prolonged engagement.
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Offer intuitive budgeting features that enable spending goals on a global, category, or sub-category level.
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Enable automatic budgeting based on a customer’s spending history.
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Allow customers to drill down into sub-category spending to see exactly where their money goes.
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Provide easy-to-consume weekly and monthly reports for a high-level overview.
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Track and manage all cards, accounts, and transactions from any source with a Google-like search.
Curious to learn more?
Contact us today to see how you can position your bank as a value-adding advisor with our personalisation and PFM tools.
