“With Oracle Intelligent Advisor we could offer a service that allows us to provide, in real time, the right information to users who are asking for allocations. We wanted the requester to be informed very early and autonomously in order to reduce calls and physical contacts.”
France’s Caisse Nationale des Allocations Familiales (CNAF), part of the country’s social security program, manages and dispenses aid to families in the form of income supplements that are designed to reduce financial vulnerability and poverty. Every year the organization disburses close to 83 million euros in family allowances.
To process claims and calculate how much to pay each beneficiary, the agency used a legacy system running on mainframe infrastructure. The process was time-consuming and left citizens in the dark as to how their allowances were estimated. CNAF needed to automate its calculation process, especially when faced with reforms by France’s government, which increased the volume of allowances to be calculated without providing any additional means. Thus, the agency implemented Oracle Intelligent Advisor in 2017 as a quick and simple solution allowing self-calculation in order to tackle this challenge.
In 2020, as a result of a major government revamp of the housing allocations law, CNAF expanded the use of Oracle Intelligent Advisor to the task of managing rent and mortgage allowance calculations based on citizens’ most recent incomes.
However, CNAF did not yet have accurate estimates regarding the required infrastructures to support this new process. The organization realized that refreshing its on-premises data center could be a wasted investment if it miscalculated demand and was unable to scale. Choosing cloud technology was motivated by the need to quickly build an environment without a negative impact on the existing system.
Oracle Intelligent Advisor provides us with a scalable solution to update family allowance calculations based on Social Security reforms.
Why CNAF chose Oracle
A pilot proved that Oracle Intelligent Advisor could scale to meet the demands of the public using CNAF’s services. The organization found that Oracle’s solution was widely meeting the government’s requirements.
Oracle Intelligent Advisor assists CNAF in processing one billion income and housing allowance claim calculations per year.
By applying new laws to allowance calculations and reports, Oracle Intelligent Advisor equips citizens to understand their individual allowances and produce estimates in less than one second before beneficiaries submit their claims. Oracle Intelligent Advisor has scaled to provide 11 million citizens with automatic estimates of their eligibility for a social benefit, an initiative to compensate people with modest resources.
Using Oracle Service and its capabilities suite, including Oracle Intelligent Advisor and Oracle Knowledge Advanced, CNAF is a pioneer in the public sector in offering comprehensive “digital” service experiences to citizens. Further, Oracle Service proved that it could meet the government’s benchmark of loading and processing data on 7 million housing allowance applicants within 19 hours.
CNAF’s service transformation also focused on reducing the workload of call center agents. Oracle Knowledge Advanced offered prescriptive, self-service guidance to users. The knowledgebase has been accessed by 500,000 monthly users, and the volume of customer emails was significantly reduced.
Oracle Knowledge Advanced also improved the efficiency of CNAF’s service representatives. Faced with increasingly complex payments eligibility, agents used the knowledgebase to provide relevant and contextual answers according to attributes and keywords found in the customer’s inquiry.
By delivering digital-first service with Oracle, CNAF modernized its service delivery approach and enabled the public to navigate more seamlessly through the complex social security system.
About the customer
CNAF, France’s National Office for Family Allocations, distributes social benefits to more than 30 million beneficiaries through 101 offices.