Managing customer complaints is a critical responsibility for any credit institution. It is far more than simply replying when a customer reports an issue. Instead, it is a structured, rule-based process with direct implications for trust and reputation. Banks must comply with regulatory requirements set by supervisory bodies such as the Bank of Italy and the European Banking Authority, including:
- specific response deadlines
- full traceability of every complaint
- complete transparency towards customers
However, effective complaint management goes well beyond regulatory compliance. Each report highlights something that has not worked as expected. A well-designed process can therefore also become a powerful tool for improving internal operations.
Banks receive hundreds of messages every day through multiple channels emails, online forms, chats, and dedicated platforms. These range from simple information requests and suggestions to detailed reports and formal complaints. Handling this volume quickly and accurately, particularly for complaints, can provide a significant competitive advantage. A streamlined end-to-end process reduces the risk of sanctions, boosts operational efficiency, and improves customer satisfaction and loyalty, ultimately strengthening the bank’s reputation.
Traditional complaint management: risks, delays and operational burden
Carrying out this work without technological support, particularly without AI-powered Text Intelligence, is increasingly unsustainable. The process demands considerable time and manual effort, including:
- reading communications and any attachments
- determining the correct category and the appropriate response
- transferring messages to the dedicated management system
These tasks represent only a fraction of what could be automated, yet they are still performed manually in many institutions. The result is wasted time, misallocated human resources, and inefficient complaint management.
For customers, this often leads to:
- slow or missing responses
- inconsistent classifications
- excessive workloads for staff, usually due to repetitive tasks
Such problems inevitably influence the customer’s perception of the bank. That is why solutions capable of intelligently classifying and routing incoming communications are becoming increasingly important.
Moneying: Revelis’ solution for complaint management (and more)
Text Intelligence systems combine natural language processing with machine learning to analyse text, such as the body of an email and classify it, even across multi-level category hierarchies.
The goal is not to replace human work but to enhance it, streamlining operations and significantly reducing the time required.
With this in mind, Revelis developed Moneying, a platform that applies Text Intelligence to optimise complaint handling and, more broadly, to classify large volumes of communications.
Moneying uses advanced NLP and machine learning models to automatically interpret the meaning of a message, identify the correct category, and determine its priority.
Training these models requires previously classified messages. The larger the dataset, the better the results. However, when sufficient data is not yet available, automation can begin using:
- zero-shot learning
- few-shot learning
These pre-trained models understand a wide range of general-purpose text. As the bank’s dataset grows, they can be refined or replaced with a new model trained exclusively on the bank’s own messages.
Alongside these sophisticated techniques, the platform also supports customised rules to assign specific categories for instance, rules based on keywords in attachments, email subjects or bodies, or sender domains without engaging the machine learning models.
If a customer complains about a blocked credit card, Moneying can instantly distinguish this from an enquiry about mortgage terms or a report about a mobile-app malfunction. This speeds up processing and routes the message to the right team, reducing errors. Because the system learns continuously from historical data, its accuracy improves over time.
The result is more efficient complaint management, with:
- consistent and timely replies
- greater traceability
- fewer repeat complaints
- reduced risk of sanctions
For banks and organisations managing large volumes of email, Moneying transforms complaint handling from a manual, time-consuming task into an automated, reliable and measurable process benefiting both staff and customers.
Additional features include:
- customisable classification rules
- full lifecycle management of each case
- real-time monitoring of processing status
- tracking of pending cases
- electronic document archiving
- automated reporting for Internal Control Functions
Beyond complaints: wider capabilities
Moneying is a comprehensive RegTech platform designed to handle a broad range of needs across the financial sector, from complaints to privacy requests and documentation workflows. Its centralised approach optimises the full lifecycle of each case, from registration to resolution and archiving.
Further capabilities include:
- automatic retrieval of customer data from the institution’s central records, with support for registering cases relating to non-customers
- full traceability of all departments involved Legal, Compliance, Branches—through collaborative workflows and an auditable activity log
automatic generation of standardised documents using pre-defined templates populated with case data, ensuring consistency and speeding up responses
Monitoring and regulatory oversight
Moneying supports complaint management with continuous monitoring of critical issues and KPIs, enabling process optimisation. It also detects repeated and “invisible” complaints and prioritises requests from authorities such as the Bank of Italy and the Financial Ombudsman.
A secure corporate portal provides controlled access and allows integration with other systems. Custom dashboards show processing status in real time, and proactive alerts help ensure compliance with regulatory deadlines and internal SLAs.
The future of text intelligence in banking
Text Intelligence is becoming a strategic lever for smarter, more proactive, data-driven complaint management. As language models evolve and integrate with generative AI, banks will be able to:
- deliver personalised, context-aware responses instead of relying on fixed templates
- analyse communication trends to monitor sentiment and identify reputational risks
- anticipate operational issues before they escalate
- integrate text analysis with predictive models to prevent dissatisfaction
- personalise services by identifying customer needs and suggesting targeted solutions
Investing in Text Intelligence today means building a more responsive and intelligent banking model one where complaints are not merely regulatory obligations but opportunities for improvement, innovation, and stronger long-term customer relationships.
Author: Domenico Rodilosso
