Through automatic document analysis solutions that use AI text analytics techniques, it is now possible to optimize claims management in the banking and financial sector, minimizing the time for the analysis of communications by customers and increasing the productivity and effectiveness of the complaint management process.
These solutions are enabled by the use of natural language processing (NLP) techniques and the application of machine learning algorithms that support textual classification and information extraction from documents.
The automatic analysis of large quantities of documents allows financial organizations (banks, insurance companies, fintechs) to improve the management of customer relations and consequently Customer satisfaction.
The operational scenario: collection and management of customer communications
Every day banks, insurance companies and fintechs receive large amounts of documents from customers, via email, social channels or other contactability tools. These are communications expressed in natural language, which can be of various types:
- Requests for information on the products/services provided
- Satisfaction Feedback
- Complaints
- Claims
Currently, the management of this documentation takes place through the manual analysis of specialized employees who read the individual communications, establish their type, and sort them to the competent offices.
The manual analysis process determines a series of critical issues, such as:
- Long processing times, due to the need to read the individual communications received
- Subjectivity in the classification of communications – it is possible that, in the face of the same communication by a customer, two different operators attribute different types to the same
- Inefficiency of the claim management process: the slowness of the analysis phase and any errors in the classification phase determine longer times in the management of claims, which in some cases exceed the limits established by the laws and therefore determine an economic damage for banks and insurance companies
- reputational damages and reduction of Customer satisfaction: customers who perceive delays in the management of their requests tend to look for alternative solutions.
To overcome these critical issues, advanced text analysis capabilities have become indispensable for all financial organizations, which through AI text analytics solutions, can automate the interpretation, classification and extraction of information from texts, thus transforming the enormous amount of documents received into a resource capable of generating a decisive competitive advantage.
How to implement an AI Text Analytics solution for claims management
The automatic analysis of customer communications is based on the availability of computer applications that can:
- Collect documents, interoperating with the tools and platforms responsible for managing customer contactability
- Analyze and interpret texts, through the implementation of natural language processing (NLP) techniques
- Classify documents, through Machine Learning models trained specifically to identify the cases of interest to the financial organization
- Automate document forwarding to right people and customer response processes
These AI applications must be based on highly scalable architectures, which make it possible to handle high and highly variable workloads.
Revelis has created Moneying, a Regtech platform that provides a module for the management of claims and complaints. Moneying is based on a distributed and scalable architecture, and is now successfully used in several organizations of primary importance in the field of electronic payments.
The added value of the management of textual communications based on Artificial Intelligence techniques is related to the possibility of going beyond the automation of processes. Today, in fact, to be competitive it is not enough to provide quality financial services and have adequate customer management, but it is necessary to have forecasting and anticipatory skills, borrowed from the deep understanding of the available data.
For this reason, the Moneying platform provides Banking Business Analytics features that offer:
- Descriptive Analytics, which aim to understand the current state of the business
- Predictive Analytics that, based on trends, predict future outcomes
- Prescriptive Analytics, which generate recommendations (regarding the management of situations similar to those already experienced
Conclusions
Textual content now represents more than 90% of the data available in financial organizations, and extracting information from documents is a “goldmine” for insurance companies, banks and fintechs.
In particular, the management of customer claims and complaints based on AI text analytics allows cost and time savings, and therefore has a significant impact in a highly dynamic and competitive operating context.
If you would like to receive support for the use of data for claim management, please contact us. We will show you all the possibilities that artificial intelligence solutions offer to companies to create value and competitive advantage using their own data.
