Automating customer service and analysis with AI in financial settings

Financial institutions increase operational efficiency and customer experience with intelligent service solutions and conversational analytics

Institutions in the financial sector were looking to increase the efficiency of service processes and improve the user experience by applying artificial intelligence to relationship channels. The aim was to automate recurring interactions, structure analyses of service journeys and use conversational data as a basis for the continuous evolution of services.

The challenges: increasing operational efficiency and analytical intelligence in service channels

Organizations needed to modernize their customer relationship channels, reducing their dependence on manual processes and increasing their ability to analyze interactions carried out in digital environments.

Among the main challenges were

• Automating service processes while maintaining the quality of the user experience;

• Structuring analyses of customer service journeys and customer interactions;

• Reducing operational effort in support and ombudsman channels;

• Increase visibility of conversational data to support decision-making;

Use artificial intelligence as the basis for continuous evolution of the digital experience.

Modernization required integration between conversational automation, analytics and critical operational processes in the financial sector.

Why Foursys?

Foursys was chosen for its ability to structure artificial intelligence solutions applied to customer relations and conversational data analysis in highly complex financial environments.

Among the main differentials considered were:

• Experience in implementing customer service automation solutions in the financial sector;

• Ability to integrate conversational channels and corporate processes;

• Specialization in structuring analytics applied to relationship journeys;

• Work aimed at continuous improvement of the user experience;

Strategic use of conversational data as input for operational evolution.

Success factors

The execution of the initiatives was supported by a structured approach to conversational automation combined with strategic analysis of the interactions carried out on digital channels.

Among the main success factors were:

• Automation of customer service for the bank of an automotive manufacturer;

• Optimization of ombudsman processes with the support of artificial intelligence;

• Implementation of bot analytics to analyze customer service journeys at an insurance company;

• Structuring operational indicators based on conversational data;

Use of insights generated by bots to continuously evolve the digital experience.

The solution: conversational automation and analytics applied to the customer experience

Foursys implemented structured service automation and conversational analytics solutions aimed at improving operational efficiency and the user experience in financial sector institutions.

The solution included

• Automation of customer service processes for the bank of an automotive manufacturer;

• Optimization of ombudsman flows with the support of artificial intelligence;

• Development of bot analytics for a large insurance company with a focus on process and relationship analysis;

• Structuring indicators based on conversational data;

Providing strategic insights for the continuous evolution of the user experience.

Results

The application of artificial intelligence to relationship channels led to significant gains in operational efficiency and improved user experience:

• More agile and intelligent service;

• Improved user satisfaction;

Strategic use of conversational data as a basis for the continuous evolution of digital services.