Financial institutions are at a crossroads: While generative banking AI offers unprecedented opportunities to streamline operations, enhance customer experiences, and drive innovation, it also presents unique risks and challenges. This is especially true for high-trust industries like banking, credit unions, and insurance.
Here’s the good news: IT leaders, chief operating officers (COOs), and risk teams can confidently deploy and maintain banking AI—they just have to choose the right solution.
Read on to discover five things you should look for (and questions you should ask) when looking for the right AI partner:
1. Proven Results in Financial Services
A vendor’s track record matters. Look for solutions that have demonstrated measurable impact in the financial services industry. Proven case studies and testimonials from similar institutions indicate whether a solution is tailored to your needs.
Ask these questions:
- Can your vendor provide specific examples of how their solution has driven ROI for financial institutions?
- Are their tools designed to address industry-specific challenges such as compliance, fraud prevention, and customer trust?
- Is their banking AI leveraging data specific to your use case and industry?
2. Security and Compliance as Core Capabilities
Handling sensitive customer data requires strict security measures. Be sure your AI solution aligns with your compliance requirements and regulatory frameworks. Look for banking-specific AI that offers:
- Encrypted data transfers and secure storage protocols.
- Assurance that customer data is not used to train third-party models.
- Robust monitoring and logging for auditability and governance.
- Peace of mind that customers will not experience gen-AI hallucinations.
Ask these questions:
- How does the vendor protect sensitive data during and after interactions?
- What mechanisms are in place to mitigate risks like data leakage or unauthorized access?
- How can I be 100% sure that this solution will not generate an incorrect answer and put it in front of my customers?
3. Scalability and Flexibility
Banking AI solutions must not only meet your current needs but also scale as your business grows. A scalable platform ensures your organization expands AI capabilities without costly overhauls. These features indicate your solution will grow with your financial institution:
- Modular architecture that allows for phased implementation.
- An intuitive content management system that makes it easy to add new topics for your AI assistant to cover
- Ongoing maintenance and AI training that ensures the AI model doesn’t become obsolete over time
- Support for expanding interaction channels, from chat to voice to video.
Ask these questions:
- Can the solution scale to accommodate growth in customer interaction volumes?
- How easily does it integrate with our current technology stack?
- How often do AI data experts annotate new data samples for your LLM?
- How many training and testing cycles does your LLM undergo each year?
- Can I add new topics to the chatbot myself, without submitting a ticket for a technical team to manage it?
4. Practical, Turnkey Implementation
Complex banking AI deployments can strain internal teams. Look for solutions that minimize complexity and deliver value from day one without requiring extensive AI training or customization. Must-have features include:
- Prebuilt models and workflows tailored to financial services.
- Intuitive tools that empower non-technical users to deploy and manage AI.
- Minimal onboarding time with clear, guided implementation.
Ask these questions:
- How quickly can the solution be deployed, and what is the average time to value?
- Does the vendor provide ongoing support during and after implementation?
- Are you building and training an AI language model specifically for me, or am I leveraging a language model that is pre-trained?
- How many real-world utterances has your LLM been trained on so far?
5. Insightful Analytics and Reporting
AI should empower your institution with actionable insights. Ensure your vendor offers intuitive, real-time analytics that allow all stakeholders to access the data they need to make informed decisions. If an AI solution offers the following, you’re on the right track:
- Conversational interfaces that enable natural language queries for insights.
- Real-time performance monitoring and trend analysis.
- Role-specific dashboards for agents, managers, and executives.
- Insight tools that enable you to quickly and easily analyze unstructured data, without having to rely on specialist ‘analyst’ resources.
Ask these questions:
- How does the solution simplify access to insights for non-technical stakeholders?
- Can the platform provide real-time analytics that influence decision-making?
Fast, Safe, and Measurable: The Responsible AI Advantage
With so many questions to ask, finding the perfect solution might feel impossible.
But it’s not.
Introducing Responsible AI—a safe, turnkey, and proven solution tailored to financial institutions. Here are three elements of Responsible AI that separate it from ordinary AI solutions:
- Security: Responsible AI models run within Glia’s infrastructure, ensuring no sensitive client data is used to train third-party AI models. TLS encryption safeguards data transfers, and triple hot redundancy ensures reliability.
- Fast Implementation: Responsible AI solutions are purpose-built for the specific use cases of financial institutions, making them faster and easier to deploy than all-purpose AI solutions.
- Measurable ROI: Financial institutions using Responsible AI report significant improvements in efficiency, customer satisfaction, and cost savings.
- No Hallucination Risk: Responsible AI models selectively leverage generative AI to cut time and complexity out of internal-facing processes, but never put generative AI unchecked in front of a customer.
Responsible Banking AI in Action
Peace of mind, turnkey usability, and measurable ROI may sound good to be true, but they’re real tenets of Responsible AI. These three case studies illustrate real-world examples of how they play out:
Better Service, Happier Customers
Too many calls, overstretched reps, frustrated customers, and high abandonment rates: Faced with it all, this client deployed the Glia Virtual Assistant (GVA) to automatically handle routine inquiries. The GVA resolved 60% of inquiries autonomously and reduced handle times by 30%. Most importantly, by freeing up agents to handle complex interactions, the GVA generated a 20% improvement to CX scores.
Faster, More Productive Agents
One Glia client needed to make life easier for agents who were spending excessive time searching for information and completing repetitive tasks. This client deployed Agent AI to streamline workflows by automating administrative tasks and providing real-time coaching. As a result, the client cut average handle time (AHT) by 10% and created a 35% increase in agent productivity.
Managerial Excellence with Exec AI
For this Glia client, contact center leaders relied on business intelligence teams to analyze data, leading to delays in obtaining actionable insights. Glia’s Quality AnalystGPTchanged everything. Using the tool, contact center leaders could ask questions in everyday language to instantly gain the data they needed. This resulted in faster decision-making time, more complete and fair analysis, and improved strategic planning efficiency.
Put Glia’s Banking AI to the Test
Want to see if Glia is the right AI solution for your financial institution? Now you know the right questions to ask. Get in touch today to see if we’re up for the task.