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ABOUT THE CLIENT
Credit Agricole is a universal international bank that has been operating in Poland for nearly 20 years.
Credit Agricole Bank Polska is one of the most frequently recommended banks in our country. It operates in the area of retail, corporate and agricultural banking, small and medium-sized enterprises, and in the area of Consumer Finance.
Credit Agricole Bank Polska is part of the Credit Agricole Group that belongs to the 10 largest banks in the world in terms of asset value. This group operates in 48 countries globally and serves over 52 million customers.
Credit Agricole Bank Polska faced challenges related to enhancing efficiency in assigning customer cases, prioritizing them, and preparing replies.
Credit Agricole Bank Polska has been our long-term business partner whom we effectively support in the field of technological solutions. This time, the bank turned to us with its challenges regarding dealing with customer requests. The first challenge was the classification of incoming documents. The manual handling of a large number of requests led to them being assigned to the wrong categories within the company’s system. This had consequences in the form of request processing delays, potential financial penalties from regulatory authorities (e.g. due to failure to meet the reply deadline) and decreased customer satisfaction retention rate.
The second challenge was to prioritize responding to customer requests. The incoming documents covered not only a wide range of issues to be resolved, but also a varying degree of the customers’ emotional intensity (e.g. a customer asking for help vs. a customer dissatisfied with a service). Assessing these two matters by a human made the task highly time-consuming. This, in turn, affected the request processing time.
The third challenge involved preparing replies to customer requests. It was a long and arduous task; it should be also noted that preparing a reply to a non-standard request took 2.5x more time in comparison to standard requests.
reducing the risk of incorrect request assignment in the bank’s internal system
eliminating the risk of a request getting stuck at one of the processing stages
reducing the long response time in terms of customer requests
increasing the correctness and accuracy of replies to standard and non-standard requests
speeding up prioritization based on the case type and emotional intensity
automation of tasks previously carried out manually
SOLUTION
ROC3 is an innovative solution that supports the handling of complaints and requests as well as other after-sales processes. To achieve this, it uses artificial intelligence (AI), especially natural language processing (NLP), natural language generation (NLG), and machine learning (ML) algorithms.
The reason for using ROC3 is to shorten the time needed to prepare replies to customer requests (up to 50%), and full automation of standard operations regardless of the style and format applied to the customer requests. In addition, the solution turns unstructured data from the incoming requests into knowledge about the customer (e.g. satisfaction with the services or customer care). An additional advantage is the system’s ability to independently recognize some cases and execute appropriate orders, e.g. withdrawing marketing consents at the customer’s request. In such cases, no employee time or attention is required. Achieving this level of automation is guaranteed by three basic ROC3 modules: Extractor, Classifier, and Generator.
Significantly shortening the time of handling complaints and requests while increasing customer satisfaction regarding after-sales processes
Automation of certain tasks of Back Office employees to eliminate case categorization errors and reduce repetitive activities so that the employees can deal with tasks requiring human creativity and decision-making
Shortening response times as per SLAs (up to 50%) thanks to predefined answers, even for unusual cases
Standardizing replies and ensuring compliance with simple wording rules to guarantee easy-to-understand communication with customers
Identifying customer intentions regarding the execution of obvious orders by robots (RPA) instead of engaging employees
HOW DID WE DO IT?
a team permanently cooperating with CABP, responsible for developing the IT architecture, CI/CD (DevOps)
a team of data science and AI architects, responsible for the entire implementation, AI algorithms and MLOps
Director of Customer Service Quality Development Department
Director of Innovation
Leader in the Customer Advocate Office
Configuration Management Senior IT Specialist
Senior Business Process Robotization Specialist
Data Scientist
Machine Learning Engineer
DevOps Specialist
NLP Specialist
Due to the project’s innovative nature, we jointly decided that the best solution would be implementing the project through the Agile methodology. This methodology was intended to translate into quick software implementation and close cooperation between the teams. The iterative mode of work allowed for flexibility in product deployment.
As part of this project, together with the CABP team, we expanded the Remedy system used by the bank to manage the entire after-sales customer service process. In this system, we have embedded a Widget, i.e., an AI-based Writing Assistant (an assistant supporting the writing process based on artificial intelligence).
In addition, Remedy uses all available information classifiers and extractors. It places the extracted materials in its own database. The widget finally generates a print-ready DOCX file containing the employee’s signature and the customer’s personal data.
The work was carried out – per the Agile methodology – in two-week iterations, resulting in subsequent versions of the application that provided new functionalities. The teams met regularly for workshop sessions to review and discuss the effects of their work. Together, we clarified the requirements for the following project stages.
HOW DOES THE SYSTEM WORK?
Using ROC3, employees can save time and energy on duties such as receiving and classifying customer requests, prioritizing them, and preparing the reply text. The system will perform most of these tasks for them, while employees only need to verify the created documents. ROC3 contains three main modules; each is responsible for a specific work stage regarding customer requests: Extractor, Classifier, and Generator.
Receipt and preliminary analysis of documents
Classification and prioritization
Support in composing the reply content
Since the introduction of ROC3 to the system, Credit Agricole Bank Polska has been enjoying many benefits. First of all, the response time regarding customer requests has decreased. In the case of simple requests, customers receive an answer right away because the system executes their orders independently. More complex cases, including non-standard ones, are handled faster than before (up to 50% faster). This positively affects customer satisfaction levels.
Another significant factor for Credit Agricole Bank Polska is compliance with regulations imposed by authorities such as the Polish Financial Supervision Authority. This applies, i.a., to the rules for processing requests which are set out in a specific legal act. Thanks to speeding up customer request processing, the bank can always meet the deadlines for SLA-related tasks and avoid financial penalties imposed by KNF.
The last important issue was to increase the reply correctness and enhance customer satisfaction. The combined forces of the bank’s employees and ROC3 ensure that the replies are correctly composed. In addition, all issues raised by the customer will be considered and duly handled (no case can escape the AI). Correctly written replies relating to the customer’s case positively affect the bank’s image and customers’ willingness to continue cooperation.
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Business Development Specialist
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