A global provider of insurance services and financial services AXA Group works tirelessly to turn innovations into improvements, by exploring, learning and playing with new ideas. With AXA XL the property & casualty and specialty risk division of AXA, the company looks to push boundries and shape the future of the global insurance industry with innovative products.
EdgeVerve Systems can continue to help you work towards a better future with the power of AI and Automation. We can help you meet your bold ambitions by leveraging the capabilities of AI and Automation, thereby bringing transformation across your globally spread services and operations.
Based on our research and expertise, we have identified a few AI and Automation game-changers to help transform your business with speed and agility:
We have everything you need to help you navigate the future with our AI platform, AI-enabled business applications, and our leading Automation platform, AssistEdge.
AXA has a strong distributor, broker and partner network for policy sale and distribution. Getting into contracts with them and different service providers, vendors, service partners and external sales channels which can run into thousands.
Contract negotiation, tracking and review to ensure contract compliance are key activities involving a sizable team of lawyers, process SME’s and is a manually intensive process. The contracts are majorly present in unstructured data forms as scanned documents and these contracts govern how business is conducted and governed by the law of the land. Not understanding legal obligations could lead to legal issues and in turn to incurring unnecessary expenses.
Since the number of contracts remain high, in-time renewals remains a big challenge. During disputes, the digitized information brings transparency and eases the flow of information.
Currently managing contracts with all its partner networks is a human intensive process and involves high costs incurred as fees of legal experts and legal process SME’s. The process is also slow and time consuming.
The need is to automate the contract analysis and review process to get better visibility and searchability across contracts to identify the right clauses to watch out for, to keep a track on expiring contracts, or to identify any policies being violated by any of the involved parties in the network.
Nia Contracts Analysis ingests pertinent information from all contract agreements and captures relevant information from all contracts and other related documents such as contract clauses, policies and expiry. Knowledge models are created to capture contract rules, exceptions and resolutions. The knowledge model learns and evolves with operational knowledge and contract exceptions and has the capability to extract information from tabular data spanning across pages and handling capabilities like nested tables and merged cells. It provides the ability to handle complex contract document extraction and reviews.
A responsive Nia Chatbot interface with natural language can be provided to handle queries from users related to the contracts.
Digitization of contracts and automation of contract extraction
Saving high legal fees for contract reviews
Reduction in turnaround time
Improved compliance with contractual obligations
Comprehensive Risk Assessment module to identify your risk exposure through various Contractual terms and clauses
productivity increase for staff
contracts processed in two weeks
Processing the historic load of 24K+ global procurement contracts with Risk Scoring for all contracts, suppliers and document types.
Reduction in research effort & Time
increase in productivity
Contract Cycle time reduction
AXA has about 108+ million clients who on consuming a medical service, the members submit their claims to the insurer. Claims are received through various channels including mobile apps.
At the insurer end, claims processing is a key operational activity. There are scanned receipts received with a claim which are typically embedded or attached in the claim document. It requires manual intervention to extract and validate data and manual interventions pose the usual challenges like adding delays in the claim processing, manual errors and additional auditing needs. Processing claims involves reading the claims form, invoices, policy document and the underwriting authorization letter. Most of the process can be extensively automated using RPA and other backend workflow integrations.
Given the volume of claims to be processed and the need for the manual processing, the insurer ends up having a large operations team for claims processing.
The need is to have an automated mechanism for data extraction and validation from the claim documents to reduce the manual effort.
The system should be able to perform straight through processing and in case of accuracy issues should be able to learn from human correction i.e. have the capability of Machine Learning based learning.
Nia DocAI helps with data extraction from a wide variety of document formats, automated classification of different documents, section identification and extraction of required fields ensuring accuracy. It provides a learning & review workbench for display of extracted data and human review for continued learning. Its access control provides a persona and workgroup based document display on the workbench.
NIA DocAI makes the extracted data available to the downstream application in json message format.
AssistEdge RPA’s software robots act as a downstream to DocAI and consumes the data for further processing. Its rich set of technology adapters and strong automation capabilities help not only in reading the document data received in the json but also in doing an end to end automation of repetitive tasks. It has capability to validate and massage the data, applying business rules, login to applications with specific roles, feeding data into ERP and multiple other systems and performing further tasks in the business process.
This helps in significantly improving the process efficiency with automation and making them as touchless as possible.
Suite of AI capabilities (OCR, Vision, NLP to name a few) to digitize documents of varying types and complexities
Data extraction with up to 80-90 % accuracy
Average handling time of extraction (AHT) reduced by 50%
Automated Claims Processing from claims requests of varying complexity, image quality, templates and structures for over 2000+ claims per day.
More than 90% accuracy.
Expedited loan processing by digitizing 100K+ documents across a vast loan application database of 25K+ folders in 12 days.
As one of the largest global insurers and being a large corporation AXA has a different set of IT challenges with digital transformation being one of the key IT initiatives. With hundreds of applications within the organization and it undergoing digital transformation, maintaining business continuity is an uphill task. While the common application stack is maintained by AXA Go, the situation will not be too different across geographies. This has the potential to impact customer experience adversely, if not handled efficiently. In such scenarios, it is observed that the number of tickets related to IT systems (specially for L2 and L3) also increases significantly. IT Support team works round the clock on ticket resolution to reduce noise in production and to meet the SLAs.
In IT Operations scenarios, it is observed that the majority of the tickets have some patterns and their resolutions are also standard.
With a lot of data getting generated in the individual applications as logs for every event there is a lot of historical ticket data available in the system that remains unexplored. It is difficult and challenging to manually go through the data and identify the pattern or co-relations between them to derive insights. Due to the lack of this analysis, operations teams continue to invest a significant amount of time in analyzing every problem individually.
There should be a system, which consumes the historical application logs and ticket data to create co-relations leveraging AI capabilities and helps in identifying the root cause for commonly occurring failures. There is a need to have an automated way of remediating these tickets and where possible, predicting these failures and also fixing the issues before they occur. If a failure has occurred and automated handling is not possible, for manual ticket handling, the operations team should be able to easily analyse and fix the problem.
Nia AIOps brings the power of AI and Automation to IT operations leveraging the Nia AI platform capabilities like Machine Learning, Natural Language Processing and Computer Vision. It employs powerful AI models for ticket analysis, enrichment, pattern detection and anomaly detection. Nia AIOps enables IT Organizations to move towards predictive maintenance and autonomous operations with its state-of-the art anomaly detection and self-heal orchestration capabilities. Nia AIOps offers comprehensive end-to-end capabilities for holistic and continuous IT Operations improvement to solve simplistic use cases that need RPA capabilities or runbook automation to the most complex ones that need the convergence of AI and big data.
It collects the information from various applications including ticketing systems (ServiceNow) to:
Detect patterns and anomalies in real-time by correlating data from various systems
Monitor tickets in real time and route them to the right support queues
Identify impacted modules and applications
Auto resolve the ticket, where feasible, by orchestrating the necessary remediation steps
Intelligent Automation of IT operations
Saving critical man-hours in issue resolution resulting in significant cost savings
Predictive maintenance by identifying anomalies and orchestrating resolutions
Improvement in MTTR by 90% in specific cases
reduction in SME dependency
IT Ops Effort reduction
1 Mn USD
per annum savings
reduction in application outages
TAT improvement for repetitive tasks
monthly ticket volume automated
Consistent SLA adherence of 99-100% leading to customer delight
Man-hours saved over 2.5 years
reduction in manual activities across 7+ LOBs
Tickets resolved automatically
Improvement in Productivity
In the age of digital transformation, large companies like AXA look at automation as the first and key lever to optimize every area of application maintenance & support operations including testing and validation, using varies tools for performing test automation.
Traditional test automation has some limitations on various fronts, for example, different tools support automating a particular type of application testing like Selenium for web applications and UFT for web and desktop applications. Whereas the RPA approach is platform independent and can be used to automate web, desktop, mainframe based applications and those running on virtual machines like Citrix.
The traditional test automation approach is also costly in the long run and requires high technical expertise to write the test scripts and has limited configurability which is not all encompassing.
The need is to have a comprehensive automated testing capability to handle:
Life Cycle Test Automation – Business Flow Automation involving multiple technologies and platforms
Test Data Management – Test data generation involving multiple applications, time intervals, realtime and transactional
Citrix / Legacy Automation – surface automation image recognition and OCR
Unstructured Data Processing – Bank statements, letters, cheques and inbound and outbound correspondences
Non-Core Automation – Reconciliations, Job Monitoring, Metrics Reporting and Data Migrations
User Acceptance Testing – Business centric test automation
RPA brings the focus back to the Business Process by simplifying the Test Automation process. By unifying ease of development with advanced features such as AI & Cognitive Analysis, AssistEdge RPA bots are able to achieve levels of test automation never seen before. It enables
Using AssistEdge RPA to augment test automation not only reduces dependency on human resources, it brings flexibility in test scheduling, monitoring & control, eases debugging failures and makes testing scalable.
reduction in scripting efforts
reduction in maintenance efforts
Unattended test execution
Mitigating Risks and Driving Revenues with effective Contracts Analysis
Process discovery remains a manual activity and takes up precious SME time and effort and may also introduce human bias. Process automation is one of the key levers for reducing the cost of operations and making them efficient. After starting the automation drive, building process pipeline for automation is key to scale the automation initiative. Most of the times, there is no clarity on the sequence in which the processes should be picked for automation. Picking up a process for automation, without taking into consideration the variations, might lead to building automations which are not robust. Knowledge of the exact process is required as the documented SOP’s may not be up to date. Processes executed by the operations team on the ground may not be the exact documented process and the variation scenarios in the process may not be captured.
The need is to have an automated and scalable way of discovering the processes along with its variations and having a prioritized process pipeline which can then be picked for automation.
AssistEdge Discover client is deployed on the desktops of operations agents of different processes. It captures the user interactions with systems, the click streams and keystrokes. The data from different agents performing the same processes are combined and run through AI algorithms, which capture the exact process executed. This helps to identify the process map and blueprint of the process along with the alternate paths, frequency and time taken per step. The process maps are validated with SME’s in significantly less time and application control metadata captured from agent interaction with the application helps build the automations faster. This also provides insights on which process is to be considered for unattended automation (RPA) and where attended automation (Engage) provides better ROI.
Rapid process discovery and identification of automation opportunities at large scale
Significant reduction in process discovery time
Reduction in failures in automations since process variants are automated too
Automation Blueprint – an automation priority matrix
Granular visibility into processes with Interactive maps
Process insights to streamline processes and improve workforce productivity
The potential for $189k savings annually
Iidentified around 20% of process paths where user trainings can help in improving the efficiency
Root causes caused due to IT issues in the core application were identified and picked up for fixes
90% reduction in average handling time across all processes to 10min
Mitigating Risks and Driving Revenues with effective Contracts Analysis
With an increasingly complex ecosystem of technology and market information at AXA, Procurements Leaders have to tackle certain challenges to meet the rising expectations. Procurement is increasingly expected to evolve from a support function to a key strategy enabler. Critical tasks include optimizing cost, bringing in innovation, and moderating risks and costs.
91% of CPOs agree that procurement needs to be more agile to respond to market changes and 78% of Procurement Leaders say that cost reduction is their top priority. From this perspective, large organizations like AXA are looking to make procurement visible, meaningful, and simple by addressing key challenges in a way to make spend management both insights-driven and autonomous. Key challenges include:
Data spread across multiple ERP systems
Missing connections between internal and external insights
Limited Spend Visibility, supply risk and spend leakages
Lack of insights on performance issues
Critical obligations in supplier contracts that lack proactive monitoring
High Contract leakage and non-compliant spend
Limited ability to influence buying patterns
Lack of awareness on opportunities that exist
Business decisions delayed or not the most optimal
Different views across various stakeholders
ProcureEdge is an enterprise-grade, AI-powered product designed to augment the performance of various stakeholders in procurement teams. It sits on top of existing systems of record, eliminating the need for any rip-and-replace, and generates value for procurement leaders in a way to make spend management both insights-driven and autonomous.
Potential Savings with ready to Consume Analysis and Present Savings opportunities
Opportunities identified for supplier rationalization, spend optimization, payment terms, price optimization, and contract renegotiation
Gradual reduction in Leakages with periodic monitoring of internal and external risks
Access to Real-time and proactive market insights
Significantly improves Spend Visibility and compliance, thereby, improving Procurement health
Reduce Contract Leakages (Reactive and Proactive)
Increase compliance to contract obligations
Data and Insights-driven Negotiations
Over 12% in savings achieved in CAPEX / MRO against a 6.5% target
Working Capital benefit of €6.2 M through Payment term optimization
Enhanced vendor rationalization @10% of active supply base
10% Vendor Normalized
80% accuracy for ML Model for data classification
Cleansed data lake used for client specific Dashboards
Mitigating Risks and Driving Revenues with effective Contracts Analysis