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Cognizant is a global IT systems integrator and consulting company. As an industry leader in automation, Cognizant was seeking a solution to enhance its intake offering that could meet its stringent accuracy requirements while decreasing the resources required and shortening the turnaround time for processing.
The ability to give feedback to the [Intelligent Intake] model, the learnability part of the tool, is one of the most important things, otherwise; we are back to the old template-based approach because you have to go back and retrain the model.
-Abhinav Kolhe, Senior Director, Digital Strategy and Automation Solutions with Cognizant
Cognizant’s client, a prominent US real estate data provider, provides comprehensive data about real estate transactions across the US and licenses this unique data asset to critical residential real estate market players. Competition in this segment is fierce, as these documents are all publicly available. Key differentiators are the speed at which the data becomes available in an actionable format and the accuracy of that data. Historically, this intake problem was addressed with inefficient, error-prone, brute-force manual data entry; Cognizant had deployed thousands of offshore resources to process this massive volume of documents. Cognizant sought to reliably automate the intake process, identifying and extracting relevant data from unstructured mortgage title and deed documents published across the US by local municipalities – up to 40 million documents each year.
Cognizant selected Indico, with their Intelligent Intake solution, helping the client achieve 40% annual savings in processing costs. Indico’s Intelligent Intake solution is based on its proprietary Transfer Learning approach, allowing non-technical users to quickly build custom machine learning models tailored precisely to the customer’s needs. Using a point-and-click interface, SME’s can upload their training samples, create custom attributes for extraction, classification, etc., and quickly build models with Indico’s proprietary AI-assisted labeling. Most importantly, the solution had to have enterprise-scale capabilities. Indico’s cloud-native microservices architecture enables continuous optimization of the compute footprint and can scale up to any throughput or processing volumes.
Cognizant and its real estate client no longer need to compromise on intake speed or accuracy. In addition to reducing processing costs, Cognizant has gained accuracy over time. Cognizant’s goal was to augment the employees who are experienced in the titles and deeds process, not completely replace them. Indico enables these process experts to review results, with a pane that shows the level of confidence for each field as to whether the model is producing accurate results. For fields with, say, 70% confidence level, process experts can help teach the model how to increase accuracy. Eventually, those fields with high confidence ratings may be hidden from the review pane, thus speeding the process further while boosting accuracy ratings.
