1. Conversational AI
AI-powered assistants are now one of the most visible — and expected — applications in customer service. In financial services, they’re helping institutions stay available to customers 24/7, without compromising on the quality of service. These assistants can handle queries quickly and accurately.
But there’s more to it than just answering FAQs. The most advanced assistants are built to understand context, learn from past interactions, and adapt their responses to each user. They can handle complex tasks like guiding users through onboarding processes, helping them reset authentication methods, or even flagging suspicious activity. And because they’re integrated with internal systems, they can provide real-time information — like available loan offers or the status of an insurance claim — instantly.
How to implement:
- Integrate with core systems to provide real-time, personalised responses.
- Train using real support transcripts and common customer queries.
- Set clear handover rules for smooth transition to live agents.
2. AI-powered fraud detection
Personalisation in financial services used to mean addressing customers by name in an email. Today, with the support of AI, it’s about anticipating needs and offering proactive support. When someone’s spending patterns shift, when they’re approaching a credit limit, or when a policy renewal is due — AI can detect it, understand the context, and trigger the right message or support action.
This kind of real-time intelligence allows your service teams to be far more helpful — and far more relevant. Customers might receive a notification explaining why a transaction was blocked, or a live agent might already know the issue a customer is likely to raise, based on recent activity. This shortens resolution times and builds real trust.
How to implement:
- Combine transaction data with user behaviour to flag anomalies.
- Trigger real-time alerts with clear options for user confirmation.
- Review false positives often to keep friction low and trust high.
3. Hyper-personalised recommendations
With AI, it’s now possible to deliver recommendations that go far beyond “people like you also bought.” By analysing behaviour across channels — from spending patterns to searches and financial goals — AI helps you offer products and advice that genuinely fit each customer’s life.
It’s not about guessing what someone might want. It’s about having enough relevant, real-time insight to help them make better decisions, at the right moment — whether that’s suggesting a savings plan, highlighting a better mortgage offer, or alerting them to something they didn’t know they needed.
How to implement:
- Use AI-ready platforms like Salesforce Financial Services Cloud or create custom models with AWS SageMaker or Azure ML.
- Enrich profiles by combining behavioural data from your app, site, CRM, support tickets, and external data sources.
- Build clear and transparent opt-ins for personalisation — especially important to stay GDPR-compliant and build trust.
- Test different recommendation approaches and track performance to continuously refine the model.
4. Intelligent routing for faster, smarter service
When a customer reaches out, they want answers — not transfers, delays, or repetitive questions. AI helps by understanding the nature of each request in real time and routing it to the most appropriate team, system or bot, based on topic, urgency, and past interactions.
Done right, this creates a smoother experience for the customer and a more efficient workflow for your teams. Complex cases go to the right human specialists, simple ones are handled instantly by automation, and everything feels just that bit more seamless.
How to implement:
- Train the model using your own historical data, including ticket categories, resolution outcomes and average handling time.
- Continuously review routing performance, not just by speed but also by customer satisfaction and resolution success.
- Set fallback flows to ensure unresolved queries always escalate to a qualified agent when needed.
5. Proactive customer support
Instead of waiting for customers to hit a problem and reach out, AI allows you to step in ahead of time. It could be predicting an overdraft, flagging a declined payment before it causes friction, or noticing unusual activity. AI can trigger personalised, proactive interventions.
This kind of support changes the relationship. It shows customers you’re paying attention, and that you’re willing to help even when they haven’t asked yet. It builds trust, improves retention, and often reduces support volume too.
How to implement:
- Use predictive analytics platforms like Salesforce Einstein, Pega, or build custom models using Time Series Forecasting.
- Create proactive support playbooks — define which scenarios require outreach and through which channels (SMS, email, chatbot, etc.).
- Set up automated messages for common scenarios but always offer a clear path to human support.
- Monitor which proactive actions are most appreciated and which generate the highest response or resolution rates.
6. Claims automation
In insurance, claims are a key moment of truth — and speed, clarity, and empathy matter. AI can help fast-track the process by reading documents, checking for inconsistencies, and even flagging possible fraud, reducing the time it takes to process simple claims from days to minutes.
But automation doesn’t mean cold or impersonal. Done right, it frees up human adjusters to focus on complex cases and gives customers clear, real-time visibility over the progress of their claim — something most insurers still struggle to offer.
How to implement:
- Use document processing tools like AWS Textract, Google Cloud Vision or Tractable to extract and analyse claim data.
- Set up thresholds and business rules to allow automatic approval of low-risk, straightforward claims.
- Offer a mobile- or web-based interface where customers can upload documents and track claim status in real time.
- Connect the system to fraud detection modules to add another layer of security for high-risk cases.
7. Voice biometrics
Security questions and passwords are frustrating — especially when a customer is in a rush or needs urgent support. With voice biometrics, AI listens to just a few seconds of speech and confirms identity based on unique vocal patterns, making phone authentication quick, seamless and secure.
This doesn’t just improve the customer experience. It also reduces fraud, cuts call times, and lifts pressure off support teams who no longer have to follow long authentication scripts.
How to implement:
- Integrate tools like Nuance Gatekeeper or Amazon Connect Voice ID into your call centre platform.
- Onboard users by collecting a short voice sample (often just a few seconds) during a natural conversation or a guided enrolment.
- Set thresholds and fallback options to ensure a second layer of verification when needed.
- Educate users about how it works and why it’s safe, to encourage adoption and trust.
8. Sentiment analysis
Sometimes, a customer’s words, tone or phrasing can reveal frustration long before they threaten to leave. AI can detect those signals — in emails, chats, and call transcripts — helping your team step in before things escalate.
By identifying moments of tension early, you can reduce churn, improve service recovery, and make your agents feel better equipped to respond with empathy and precision.
How to implement:
- Use NLP tools like IBM Watson, MonkeyLearn or Microsoft Azure Text Analytics to analyse written and spoken language.
- Train models using your own support transcripts or chats to increase contextual accuracy.
- Tag interactions with sentiment scores and set rules to trigger alerts or escalation workflows when negative signals are detected.
- Feed those insights into your customer success platform to help agents personalise follow-up actions.
How to implement AI in your financial services company
Before you scale any AI initiative, it’s worth pausing to make sure your approach is grounded, realistic and built for long-term impact.
Start small, iterate fast
Focus on one or two high-volume, low-risk use cases — like automated routing or proactive alerts — and track how well they work before expanding. Set clear success metrics from the start, and be prepared to adjust based on what you learn. A small success with measurable results is worth far more than a broad, vague rollout.
Keep a human in the loop
AI should enhance your customer service, not become a wall between people. Always offer clear, accessible escalation paths to human support — especially when customers feel frustrated or confused. Let your teams step in when nuance, empathy or accountability are needed.
Design for transparency and control
Make it easy for customers to understand how AI is being used — and to adjust their preferences if they want to. This is especially important when you’re dealing with sensitive data or personalised recommendations. The more transparent your AI, the more confident your users will be in the experience.
Think beyond efficiency
Automation can save time and reduce costs, but its real value comes from enhancing the customer experience. Don’t just focus on what AI can do faster — focus on how it can make interactions smarter, more relevant, and more human. That’s what sets leading financial brands apart.
Partnering for intelligent service
At Near Partner, we help financial services teams move from ideas to real, working AI-powered solutions. We collaborate closely with you to design, build and implement intelligent customer service experiences — with impact, for both your users and your business.
Here’s how we can help:
- Uncover the right opportunities by analysing your channels, processes and data flows to identify where AI can deliver real value.
- Design experiences that are clear and helpful, ensuring that automation actually improves the customer journey — and that users know exactly what’s happening.
- Build and integrate AI solutions that connect with your existing platforms — from CRMs and core banking systems to contact centre tools.
- Work iteratively and pragmatically, so you can move fast, launch with confidence, and continuously improve based on real user feedback.
If you’re ready to make customer service smarter — and not just more automated — we’d love to explore it with you. Get in touch!
