Not only healthcare industry is considered expensive and inefficient, but most of the treatments people receive are based on inaccurate diagnosis. Whereas, with the help of Data Science, the physicians can make better treatment decisions and recommend an effective preventative care. This would enable healthcare companies to significantly reduce associated costs as well.
- Diagnostics from Real-time Patient Data
- Medical Resources Allotment
- Claims Review Evaluation & Prioritization
- Medicare/Medic-aid Fraud
- Prescription Compliance
- Survival Analysis
- Medication Effectiveness
Financial sector has explosive amounts of data which can be used by the companies to offer credit with lesser risk involved. Further, companies can also use these datasets to create a baseline for spending patterns and identify abnormalities to prevent fraud.
- Credit Card Fraud
- Credit Risk
- Treasury/Currency Risk
- Fraud Detection
- Accounts Payable Recovery
- Anti-money Laundering
- Debt Collection
Data Science can help both retail and online stores to understand customer needs better and make it easier to measure creditworthiness. It also facilitates more accurate data-driven pricing decisions, giving customers the access to lower prices and advantage of increased sales to the companies.
- Market Basket Analysis
- Pricing
- Best Offer Analysis
- Merchandizing
- Inventory Management (Per Units)
- Warranty Analytics
- Location of New Stores
- Product Layout in Stores
- Shrinkage Analytics
- Cart Recommendation and Optimization
The success of a business depends as much as on its people as it does on the product. While it’s a continuous pursuit to recruit, hire, train, and manage your resources, Data science can make the job of finding the right talent for the right task far less challenging with proactive recruitment insights.
- Tracking Work Hours
- Employee Attrition
- Training Module Recommendation
- Resume Screening
- Talent Management
- Call Center Call Routing
- Call Center Message Optimization
- Volume Forecasting
- Staff Rostering
Logistics has many variables to contend with such as shifting demand, human error, traffic, fuel price, and weather shifts. We can apply Predictive logistic analytics to allow enterprises to experience reduced mechanical downtime, efficient routing, happier customers and higher priced stocks.
- Managing Demand forecasting
- Order picking from Existing Stocks
- Replenishment procurements to keep Stock Levels Adequate
- Packaging for Efficient Delivery
- Routing of Packages to Avoid Choke Points
Built ML engine to optimize an image-based search application (mobile app) by predicting top 5 closest matches. Increased the accuracy from 30% to 85% in predicting top 5.
ViewData Lake with 100TB of Storage with 7TB of data being ingested and processed weekly with a 2-class boosted Decision Tree with 87.3% Accuracy.
ViewReplaced an existing BI heuristic forecasting model by more advanced statistical modelling technique.
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