Use Cases
- Insurance
- Banking
- Retail
- Healthcare
- Gaming
- Ecommerce
Predict employee churn to improve customer relations, refine marketing strategies, and sales goals
Challenges
Predicting employee churn based on performance and behaviour to enhance customer relations, refine marketing plans and sales goals.
Solution
Deployed real-time models quickly with UnifyAI's AI Studio to provide business users with instant insights for real-time inferences, and monitoring of sales agent attrition.
Benefits
Technical Benefits: UnifyAI accelerates ML model development to predict attrition risks, seamlessly integrates with CRM and HR systems for real-time performance insights, and supports rapid building of diverse AI/ML models to analyze attrition factors.
Business Benefits: Identifies at-risk sales team members early, enabling targeted retention efforts and reducing recruitment costs. Enhances team cohesion and satisfaction, focuses on retaining top performers, and boosts overall sales productivity.
Scattered persistency models led to offline inferences, time-consuming scripts, and delays
Challenges
Scattered persistency models across data scientists' local systems caused offline inferences, time-consuming manual scripts and manual sharing, resulting in delays and inefficiencies.
Solution
Achieved higher efficiency with AI Studio to centralize various persistency models, streamlining workflows, getting real-time insights, scalability,and reproducibility - thus, simplifying model management.
Benefits
Technical Benefits: UnifyAI offers high-accuracy models for predicting policy persistency and integrates with policy management systems to analyze customer behavior. The platform supports large datasets, providing scalable analysis across insurance products and customer segments.
Business Benefits: Identifies at-risk policyholders for targeted interventions, enhances retention strategies, and strengthens customer relationships. Improves revenue stability by increasing persistency rates and reducing policy lapses.
Multiple early claim models led to fragmented management, delays, and inefficient data sharing
Challenges
Multiple early claim prediction models across systems caused fragmented management, offline inference delays, manual scripting inefficiencies, making data sharing difficult amongst teams.
Solution
UnifyAI's automated model orchestration for early claim prediction, streamlines workflows, improves accuracy, and delivers seamless integration with end-system APIs for efficient decision-making.
Benefits
Technical Benefits: AI Studio from UnifyAI accelerates advanced claim prediction models and processes policyholder data in real-time with APIs. It offers customizable models that improve prediction accuracy by adjusting to new data.
Business Benefits: Minimizes financial losses by swiftly identifying and addressing fraudulent transactions, enhances customer trust and loyalty, and provides actionable insights for continuous improvement in fraud prevention strategies.
Coordinating data from multiple sources to build accurate, privacy-compliant customer profiles
Challenges
Coordinating data from multiple sources to create comprehensive customer profiles while ensuring data accuracy and privacy compliance.
Solution
UnifyAI's AI Studio integrates and unifies customer data from all sources to create a single, comprehensive view of each customer, enables personalized interactions, prevents fraudulent claims, and aids in targeted marketing efforts.
Benefits
Technical Benefits: UnifyAI uses data-driven techniques to evaluate and identify unique customers across multiple products, integrates seamlessly with existing systems for quick decision-making, and supports tailored model development. It processes and analyzes large volumes of records efficiently.
Business Benefits: AI Studio provides a unified view of customers, enabling personalized interactions and targeted marketing. Offers actionable insights for strategic decisions, fostering better outcomes and enhancing customer engagement and loyalty.
Claim Propensity leverages AI to predict claim likelihood, enabling proactive risk management.
Challenges
Claim Propensity uses AI to predict the likelihood of claims, enabling proactive risk management.
Solution
- UnifyAI deploys predictive models to assess claim likelihood, analyzing policyholder data.
- Continuous data integration offers real-time insights into potential claims.
- Customizable models adapt to new data inputs, improving prediction accuracy.
Benefits
- Reduces risk by identifying high-propensity claims early.
- Enhances customer satisfaction through proactive risk management.
- Supports strategic decision-making with data-driven insights into claims.
Streamline KYC with facial analytics to verify identities and boost onboarding efficiency.
Challenges
Streamline the KYC process, verifying customer identities through facial analytics and enhancing onboarding efficiency.
Solution
- UnifyAI accelerates the deployment of facial recognition models for secure video interactions.
- Real-time identity verification is integrated with onboarding systems to optimize and save encrypted assets for further verification.
- Models adapt to new verification needs, maintaining accuracy and managed seamlessly through end-to-end UnifyAI platform.
Benefits
- Reduces onboarding time by automating identity verification.
- Improves customer experience with secure onboarding.
- Ensures compliance with identity verification standards.
The Risk Scoring Model assesses applicant risk, improving underwriting and reducing exposure.
Challenges
Risk Scoring Model evaluates applicant risk, optimizing underwriting decisions and reducing financial exposure.
Solution
- UnifyAI accelerates the building of models analyzing data points for risk assessment.
- OCR and NLP integration enables seamless extraction and evaluation of information.
- Instant risk scores support efficient underwriting decision-making.
Benefits
- Improves underwriting accuracy by enhancing risk assessment.
- Streamlines processes, reducing policy approval times.
- Ensures compliance with risk management standards.
Predict future sales trends for strategic business planning and optimizing inventory and resources.
Challenges
Predict future sales trends, enabling businesses to plan strategically and optimize inventory and resource allocation.
Solution
- UnifyAI employs machine learning models to analyze historical sales data, generating accurate forecasts.
- UnifyAI’s algorithms adapt to market changes, continuously refining predictions to reflect current conditions.
- UnifyAI integrates with sales platforms to provide real-time forecasting insights, enhancing decision-making.
Benefits
- Increases profitability by optimizing inventory management and reducing waste.
- Supports strategic planning by providing accurate sales predictions.
- Enhances operational efficiency through data-driven resource allocation.
Analyze historical data to uncover mortality trends and guide product design strategies.
Challenges
Analyze historical data for insights into mortality trends, informing product design strategies.
Solution
- UnifyAI accelerates the deployment of AI models to analyze mortality data.
- Continuous data integration offers real-time insights for product design.
- Customizable models adapt to new data inputs, refining analysis accuracy.
Benefits
- Informs product design with insights into mortality trends.
- Supports strategic planning with data-driven analysis.
- Enhances competitiveness by aligning products with market needs.
Aadhaar Masking secures data by concealing identity number, driving compliance and privacy.
Challenges
Aadhaar Masking to secure sensitive data by masking Aadhaar numbers, ensuring compliance and privacy.
Solution
- UnifyAI deploys OCR and NLP models to identify and mask Aadhaar numbers.
- Real-time processing ensures data privacy compliance.
- Customizable models adapt to varying data formats, maintaining masking efficiency.
Benefits
- Enhances data privacy by securing sensitive information.
- Ensures compliance with data protection regulations.
- Supports customer trust by protecting personal data.
Intelligent Policy Pricing for driving price strategies, staying competitive and profitable.
Challenges
Intelligent Policy Pricing determines optimal pricing strategies, balancing competitiveness and profitability.
Solution
- UnifyAI helps build and deploys AI models to analyze data for competitive pricing strategies.
- Real-time data analysis supports accurate pricing recommendations.
- Integration with underwriting systems supports dynamic pricing models.
Benefits
- Enhances competitiveness with market-aligned pricing strategies.
- Improves profit margins by balancing acquisition and risk.
- Supports strategic pricing decisions with data-driven insights.
Identify fraudulent applications to prevent financial losses and ensure regulatory compliance.
Challenges
Identify fraudulent applications, safeguarding against financial losses and ensuring compliance with regulatory standards.
Solution
- UnifyAI accelerates the deployment of AI models to detect anomalies in application data.
- Real-time fraud alerts are provided through integration with acquisition systems.
- Customizable models allow adaptation to new fraud tactics without compromising system performance.
Benefits
- Reduces financial losses by identifying fraudulent applications early.
- Ensures compliance with regulatory standards, avoiding penalties.
- Enhances customer trust by safeguarding against fraudulent activities.
Challenges in aggregating data from diverse sources delayed NPA monitoring and data-driven decisions
Challenges
Difficulties in aggregating and consolidating data from disparate sources, which hindered effective monitoring and management of non-performing assets (NPAs) delaying in data driven decisions.
Solution
Deployed real-time models on UnifyAI's AI Studio providing business users with instant insights for real-time inferences, monitoring of NPA and loan defaults.
Benefits
Early NPA Prediction & Risk Monitoring: UnifyAI's ML models forecast potential NPAs by analyzing transaction patterns and external data, while real-time data integration continuously tracks loan performance, offering predictive insights on customer risk profiles and automating key decisions.
Seamless Integration & Efficiency: UnifyAI's AI Studio integrates with core banking systems to streamline NPA detection and escalation, reducing financial losses, enhancing risk control, and improving operational efficiency through automated workflows and early customer intervention.
Detect anomalies in real-time by analyzing data, spotting outliers, and preventing fraud
Challenges
Detect anomalies in real-time transactions by analyzing historical data, identifying outliers, and learning from past fraud cases to predict and prevent future incidents.
Solution
AI Studio from UnifyAI boosted efficiency with centralized models, streamlined workflows, provided real-time insights to enhance collaboration, scalability, reproducibility - simplifying model management and inferencing.
Benefits
Technical Benefits: UnifyAI delivers high accuracy in detecting anomalies with advanced models, minimizing false alarms. It provides instant identification of irregular activities, enabling immediate responses, while continuously learning from new data to adapt to evolving fraud tactics.
Business Benefits: UnifyAI's AI Studio proactively reduces fraud losses by identifying suspicious activities, automating monitoring processes, and allowing banks to focus on strategic tasks. It also ensures compliance with regulatory standards through reliable reporting.
Multiple data sources, bias in manual risk evaluations, and slow credit scoring hinder adaptation
Challenges
Multiple sources for data for credit assessment. Bias in manual risk evaluations. Slow, resource-heavy credit scoring processes. Adapting to changes in borrower behavior and real-time data was challenging.
Solution
Automated model orchestration for risk assessment models, streamlining workflows, improving accuracy, and integrating seamlessly with end-system APIs for custom, efficient decision-making.
Benefits
Technical Benefits: UnifyAI automates credit evaluations, reducing manual intervention while processing large volumes of financial data. It ensures unbiased assessments, provides instant credit risk evaluation, and allows for tailored risk models that reflect specific business needs.
Business Benefits: AI Studio enhances trust through transparent credit scoring, reduces approval times, and lowers costs by optimizing the process. It minimizes loan defaults by accurately identifying high-risk borrowers, improves profitability, and ensures regulatory compliance.
Fragmented data hindered AML efforts, making detection and compliance inefficient and cumbersome
Challenges
Challenges with AML arising from fragmented data sources, making it difficult to detect suspicious activities. Existing detection methods being inefficient, causing compliance with reporting and documentation requirements to be cumbersome.
Solution
UnifyAI platform enables integration and unification of customer data from various sources to create a single, comprehensive view of data and models. Predicting and assigning risk scores to transactions and customers, while prioritizing investigations - thus improving overall AML efforts.
Benefits
Technical Benefits: UnifyAI AIStudio's machine learning models enhance detection precision, minimizing false positives and negatives. Its real-time analysis and advanced data integration swiftly identify suspicious activities, while effortlessly scaling to handle large transactional volumes for growing institutions.
Business Benefits: Automating AML processes reduces operational costs, streamlines compliance, and ensures adherence to regulations, lowering penalties. AI Studio proactively identifies risks, improving security and fostering customer trust through reliable and compliant operations.
Predicting future demand accurately prevents stockouts and excess inventory, boosting revenue
Challenges
Accurately predicting future demand for products, leading to stockouts or excess inventory, impacting revenue and customer satisfaction.
Solution
Building demand forecasting models on UnifyAI helps to analyze large volumes of transactional and marketing data thereby optimizing inventory levels in advance.
Benefits
Technical Benefits: UnifyAI’s AI Studio accelerates model development to forecast demand by analyzing historical sales, seasonal trends, and external factors. It integrates real-time data with the Data Ingestion Toolkit and scales across various product categories and locations.
Business Benefits: Optimizes inventory to reduce overstock and stockouts, boosts profitability by cutting holding costs and improving capital allocation, and enhances decision-making with data-driven insights for proactive planning and promotions.
Challenges in identifying top & under-performing products leading to inefficient resource allocation
Challenges
Challenges in identifying top-performing and under-performing products, resulting in inefficient allocation of resources and missed opportunities for maximizing sales and profit.
Solution
Using UnifyAI's AI Studio, develop predictive models for determining top and flop items among the new conceptual products on online store. This helps in planning, procurement, and achieving business KPIs by understanding the success probability of new products, in advance for better planning.
Benefits
Technical Benefits: AI Studio helped build multiple ML models to classify products using historical performance, customer reviews, and market trends. It provides real-time SmartEDA reports for dynamic marketing and inventory adjustments, with continuous learning and retraining to enhance classification accuracy.
Business Benefits: Enables targeted promotions for top products, optimizes inventory allocation based on performance predictions, and maximizes revenue by identifying high-potential products swiftly.
Unable to predict return rate accurately leads to inefficient inventory, and increased costs
Challenges
Predicting return rates accurately gets difficult, leading to inefficient inventory management, increased operational costs, and diminished customer satisfaction due to delays in processing returns.
Solution
Develops models with AI Studio by analyzing customer behavior and engagement data to predict when a customer is likely to return, allowing businesses to take proactive steps in advance to avoid returns and plan inventory.
Benefits
Technical Benefits: UnifyAI's AI Studio rapidly builds and deploys models to predict return likelihood by analyzing customer behavior, product features, and past data. It integrates seamlessly with e-commerce systems for real-time feedback and uses APIs to flag high-risk products.
Business Benefits: Proactively addresses potential return issues to lower return rates and associated costs, enhances customer experience with better recommendations, and increases profit margins by reducing return processing expenses.
Personalized size recommendations to avoid high return rates and lost revenues for retailers
Challenges
The key challenge lies in providing personalized size recommendations for customers, leading to higher return rates, increased customer dissatisfaction, and missed revenue opportunities for retailers.
Solution
UnifyAI's AI Studio helped build multiple machine learning algorithms on customer size data to predict the optimal size for products based on a customer's body measurements and fit preferences.
Benefits
Technical Benefits: UnifyAI's AI Studio builds AI/ML models for precise size recommendations by analyzing customer measurements, purchase history, and product data. It integrates seamlessly with e-commerce systems for real-time recommendations and leverages data-driven insights to refine size accuracy.
Business Benefits: AI models accurate sizing predictions reduces return rates and associated costs, enhances customer satisfaction with personalized recommendations, and boosts sales by reducing cart abandonments. Automation also improves operational efficiency and supply chain management.
An advanced system to perform patient pre-screening and give medical advice before hospital arrival
Challenges
An advanced symptoms checker system was required to perform prescreening of patients so they could have some formal treatment.
Solution
Extensive historical tabular and image data is used to train computer vision models and conversational AI chatbots to get accurate information on patient pre-screening.
An intelligent application built using text-based AI bots and computer vision technologies so that patients can efficiently chat with AI doctors to get information regarding standard treatments and schedule physical appointments.
Benefits
Conversational AI chatbots are enabled with a self-learning program to evolve from learning new data and give more optimised results.
Interactive access is given to the doctors so that they also evaluate the system and feed more data using which chatbot can learn.
A medical diagnosis and imaging system built to detect and identify anomalies in human reports
Challenges
A medical diagnosis and imaging system is built for the healthcare industry to detect anomalies in human medical reports. Helpful for doctors to quickly find defects in human organs and focus on providing more accurate treatment.
Solution
Developed an intelligence system by combining various technologies to create images of the inside of a patient's body that doctors and other medical professionals can use to diagnose and treat illnesses and injuries.
Enabled an easy-to-use chat system to create robust and reliable communication between doctors, radiologists and patients so that missing information can’t lead to a disaster.
Benefits
Collaborate with many medical and healthcare organisations to obtain a large amount of data for training the medical imaging model.
The unique architecture is developed so that it can accurately send information to doctors related to different departments and specialities.
The Cancer Imaging Archive (TCIA) is used to train the models to achieve state-of-the-art performance in the case of cancer reports.
An intelligent system to classify and determine the best embryo from each patient's embryo samples
Challenges
A complete intelligent system for extracting data from next-gen gene sequencers and processing it to classify and determine the best embryo from each patient's embryo samples.
Solution
Enabled a reliable data system which is helping in collecting and organising new data with the old collected data produced by Next-generation gene sequencers (NGS).
Advanced modelling techniques are used to identify the best embryo from each patient’s embryo samples.
Benefits
Employed a feature store to Unleash the full potential of data by keeping features organized, reusable, and accessible for further MLOps.
Multiple advanced models are trained and stored in an efficient model repository so that multiple models can become easily accessible, switchable and reusable.
An end-to-end AI system built to give associated health issue predictions on human health data
Challenges
Enabled end-to-end AI system to perform predictive analysis on human health data. Built to give associated health issue predictions with humans based on heart rate, physical activity, nutrition, spO2, and sleep information.
Solution
Prepared an interactive dashboard to enable the application to represent health and performance statistics to the customers.
Enabled health monitoring to the customers with an AI-powered health prediction model. Utilising IoT device data to provide accurate predictions on health levels and suggests actions for maintaining stability.
Benefits
Upgraded outdated in-app data dashboarding system to give a seamless user experience of observing and maintaining their daily routine and health data.
An adaptive matching system intended for the gaming industry to link players of similar skill levels
Challenges
An adaptive matching system intended for the gaming industry to link players of similar skill levels and make the game more engaging.
Solution
Developed models using streaming data that can take iterate between many possibilities to get perfect matching for a player.
Trained the models in such a way that they can match these players either on the basis of level or on the basis of similar skill.
Benefits
Enabled a game behaviour optimisation system With the player matching system so that the player matching can also be performed based on the gaming behaviour of the players.
Natural Language Processing (NLP) models are parallelly enabled inside the system to process and analyse unstructured text data such as player's chat data.
Advanced Big data and streaming data technologies are used to handle large amounts of data and perform fast data processing.
An AI-powered prediction and forecasting model to identify data patterns of gamers quitting the game
Challenges
An AI-powered prediction and forecasting model to identify data patterns of gamers quitting the game, thus reducing churn and increasing engagement at several key points.
Solution
Advanced exploratory data analysis(EDA) is performed on huge amounts of data to understand and identify the trends and patterns where customer churn occurs.
Deep learning-enabled predictive modelling was performed to accurately predict future patterns with a high likelihood of gamers quitting the game.
Benefits
The same advanced EDA was used to identify patterns and trends in customer retention, which led to identifying the causes of both customer churn and retention.
Time series intelligence system applied in parallel to forecast the optimal seasons where customer churn can be higher.
Identified several non-seasonal and seasonal key points of customer retention and predicted the positive effect of those key points in customer churn periods.
An AI-chatbot integrated into the gaming console to facilitate real-time communication among gamers
Challenges
A chatbot system powered by AI technology, integrated into the gaming console to facilitate real-time communication among gamers, thereby increasing their engagement and interest in the game.
Solution
A robust, rule and decision-based, self-learning chatbot system is developed to get fit into gaming consoles. Capable of processing chat patterns and communicating based on a vast number of already-defined rules.
Chatbot's interactive feature enables players to easily shift between team-level communications to game-level communications or vice versa.
Benefits
The system is trained to give responses mostly in challenging and funny ways so that in-game user engagement can be maintained.
Advanced self-learning methods are applied with the chatbot to learn from the gamers' conversations and give them personalized gaming experiences by customizing in-game content and adjusting the difficulty level advice.
An advanced classification system to categorize every new or former gamer based on their behavior
Challenges
An advanced classification system, developed and implemented to categorise users into distinct classes, making human behaviour optimisation an easy operation, Which can be used to determine the nature of every new or former gamer.
Solution
A specialised database is designed to collect and organise only essential data from the data silos and efficiently send it to models built to process human behaviour classification.
Deep learning models are assigned to perform the accurate classification task of users based on their behavior.
Benefits
The Data used to perform classification was unlabeled, so using state-of-the-art supervised learning algorithms prepared labelled training data for classification with less human intervention.
The final model report comes with a data dashboard and makes it easy for everyone to analyze the data and make data-driven decisions.
An automated platform equipped uses data-driven decisions to recommend relevant
Challenges
An Ed-tech organization required an AI-enabled recommendation system to give data driven recommendations of the courses to their customers.
Solution
Gathered and processed the data from various sources and built advanced data manipulation APIs to facilitate the required form of data for further processes.
A hybrid and well-evaluated state-of-the-art recommendation algorithm trained on previously collected and preprocessed data that can be scaled to handle large amounts of data.
Benefits
Deployed the model with the maintenance facilities in the back end so that system can be continuously monitored and updated as new data becomes available.
A comprehensive analytics solution to analyze and understand the overall review of every good
Challenges
A system is needed to be developed for an E-commerce platform that can help analyze large amounts of data, including over 200,000 reviews and comments on over 50,000 products.
Solution
Used statistical and computational methods to extract insights from a large set of customer feedback data, including sentiments, common themes and topics, and patterns and trends.
Applied natural language processing, machine learning and text mining techniques to build the system. These techniques are helping in sentiment analysis, topic modelling and data preprocessing etc.
Benefits
Applied transfer learning concepts and fine-tuned the models on our data so that the system can give state-of-the-art results.
Analytical system to assist e-commerce businesses in managing inventory based on critical junctures
Challenges
A highly optimized inventory system is required to better manage inventories, including over 20k products, by considering several critical points like sales patterns, demand forecasting and many more.
Solution
Enabled a cloud-based smart data storage system to store and manage the large amounts of data collected by the sensors.
Used several machine learning algorithms to analyze the data and make predictions about inventory levels and demand patterns.
Applied an AI-based decision-making system which can take into account demand prediction, real-time inventory, and other factors to optimize inventory management.
Benefits
Used deep learning methods for more accurate demand forecasting and market basket analysis to help the organization in its product offering system to increase sales.
Performed demographic clustering analysis on customer data so that the organization efficiently use the system to track the product requirement based on customer demographics like location, age, gender and preferences.
A recommendation-enabled chatbot system to provide smooth customer experience in all purchase points
Challenges
The organisation need a conversational AI to be enabled in their e-commerce platform, using which customer can quickly get comparable recommendations of different product.
Solution
Used intelligent data collection system to gather and preprocess information on products, user interactions with products, and other relevant information required to build recommendations.
Tuned and trained an already learnt model on the collected data to identify and analyse patterns in the data and make predictions about which products a user is likely to be interested in.
Integrated recommendation engine in multiple purchasing points to make recommendations to the users in real-time.
Benefits
Performed DataOps in the data system to improve the communication, integration and automation of data flows across multiple processes running inside the organization.
Applied comprehensive maintenance and updating techniques in the system to regularly updated the recommendation system with new data and get retrained so that it can continue to make accurate and relevant recommendations.