Our client is a global leader in payments and Fintech. Its reach is diverse: it caters to both businesses and consumers and plays a part in millions of financial transactions every year. We began our relationship with this Fintech giant when executives from another of our clients left and went to work for this one. Because of the efficient and professional work we had done for their former company, they brought us on board to help overcome a major challenge.
The client was experiencing a slow onboarding process of new customers. It took 11 months to onboard a new client, and that created a backlog of implementations. They requested that we reduce the onboarding process to 7 months, which we did. They also presented us with 50,000 contract documents and asked us to extract legal entities from them. We did this in 10 weeks.
We enjoyed and overcame a major challenge when working with this client. Because their data is bank-related and sensitive, we had to create a virtual environment that was synced to the main one to develop the solutions. During our performance, we were able to save the client on costs, and they were so pleased because they saw that we went above and beyond, they granted us additional contract time. We will continue to work with this client as needed and look forward to partnering with them again.
Setting up autoconfiguration for a complex Fintech client
Our global financial services provider was experiencing time issues when onboarding new clients. It took up to 11 months to onboard a new client into their services platform which created a backlog of clients for implementation. They wanted us to reduce customer onboarding time by 7 months.
We approached this issue with a two-step solution.
Configuration automation: First we created as-is configuration automation. Then we extracted the client’s configuration worksheet requirements and put them in a structured format such as JSON. Next, we converted the client’s configuration files into XML and DB-script. We then used these files to set up the financial institution instance.
Once, completed, we moved to the next step in the process.
Requirements automation: We set up a web portal to gather the client requirements. We then converted them into the client XML and DB-script client configuration files. We used those files to set up the financial institution instance.
Here are the steps we took:
As a result of this meticulous process, we identified the areas of the automation process that could help reduce the onboarding timeline. In the process, we brought innovation to our client, and they now enjoy the latest technology and enablers. Data that is entered into the requirement portal is automatically validated based on the client’s business rules and is configured behind the scenes. To improve quality configurations and reduce the defects, the rules only allow users to save valid data in the system.
Our financial services client had more than 50,000 product contract documents that we needed to extract data from. The data had to be extracted in a usable format and then migrated to their contract management system. Additionally, the client wanted us to develop dashboards on the extracted data for ongoing maintenance.
We began the process by sorting the files. The client teams shared all of the contract documents through secured FTP. Then, the offshore teams downloaded the documents on a US server. The cognitive extraction team read the documents and converted them from image to TEXT formats. Once the documents were sorted, we grouped them into client folders.
Next, we worked on extraction. For read easy entities, we used cognitive extraction. We created a document extractor (web application) to render the documents for manual users. And the complex entities were manually extracted. We next performed a quality review. Our QA team performed an audit check for correctness and completeness. We also performed a spot check on the final reviewed documents to uncover any errors. If they existed, we corrected them and saved the updates. We also created dashboards and PowerBI reports on the data extracted.
We repeated this process for each of the 50,000 contract documents. Once we had uploaded the final extracted documents, we asked our client for a review and signoff.
In all, we extracted 22 data entities from 50,000 contract documents that had 98 distinct document types—and we did it within 10 weeks. We achieved 100% accuracy and re-grouping. Also, we developed standard PowerBI reports for the client’s ongoing maintenance.
In 2018, we partnered with our client to bring their current financial system up-to-date and used innovation and efficiency to create a system that better served its needs. Our client showed appreciation by extending our contract, and we believe this is a result of our OneCompany philosophy.
We listened to their needs, analyzed the situations, and created solutions that made their systems work better for them and their customers. We are proud to have worked with this client and stand ready to provide additional services when the time comes.
With Tableau, multiple users can access the insights they need quickly and easily through a URL. The manual effort involved has completely been eliminated which saves us a lot of time, and access to both current and historical data gives us a much better picture of our customers.
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