Enterprise Grade Highly Scalable Data Engineering and Data Science Platform

Problem/Challenge – Fraud is a huge problem in the financial industry.There is fraud in almost every agency serving our nation’s citizens, from mortgage fraud to tax evasion.Nevertheless, experts predict online credit card fraud to soar to a whopping $40 billion by 2021.Detecting and preventing fraud is a huge challenge for banks given the large variety of fraud types and the volume of transactions that need to be reviewed.

Resolution – Machine learning algorithms are able to detect and recognize thousands of patterns on a user’s purchasing journey instead of the few captured by creating rules. Fraud detection process using machine learning starts with gathering and segmenting the data. Then machine learning model is fed with training sets to predict the probability of fraudwhich would therein enable federal agencies to be more responsive and save billions of dollars to taxpayers.

Why CafeBot – CafeBot’s aim is to leverage AI in accurately predicting fraudulent behavior, it will automatically engineer and identify the individual reasons for why each fraud will occur. It empowers data science teams to scale by dramatically increasing the speed to develop highly accurate predictive models with lesser number of resources. CafeBot includes innovative features including data ingestion from different data sources, data blending, data visualization, automatic machine learning and deep learning, model deployment and predictions, and interpreting the machine learning model built.

Problem/Challenge – Insurance companies conservatively lose over $80 billion per year in insurance fraud, a cost that inevitably gets passed along to individuals and businesses in the form of higher premiums. Estimates vary, but generally, a staggering 10%-30% of all claims are believed to be fraudulent.Traditional manual review doesn’t scale across billions of claims per year and rules-based fraud detection systems are expensive and slow to adapt to new fraud techniques.

Resolution – Machine learning algorithms are able to detect, recognize and prioritize likely fraudulent activity. Fraud detection process using machine learning can be used to automate claims assessment and routing based on existing fraud patterns. Additionally, you optimize customer satisfaction by not challenging innocent claims.With AI based fraud detection system in place, fraudulent claims can be flagged before they are paid which would therein reduce the cost for payers.

Why CafeBot – CafeBot’s aim is to leverage AI in accurately predicting fraudulent behavior, it will automatically engineer and identify the individual reasons for why each fraud will occur. It empowers data science teams to scale by dramatically increasing the speed to develop highly accurate predictive models with lesser number of resources. CafeBot includes innovative features including data ingestion from different data sources, data blending, data visualization, automatic machine learning and deep learning, model deployment and predictions, and interpreting the machine learning model built.

Problem/Challenge – Patients are discharged after they are treated with serious and chronic illnesses in the hospital. Once these patients leave the hospital, it is likely that up to 25% of these patients will be readmitted within 30 days to be treated again. Many of these readmissions are believed to be preventable, clinicians often lack the appropriate tools to identify which patients are most likely to be readmitted.

Resolution – Machine learning algorithms can be trained for each hospital and weighted for individual characteristics such as patient’s recent care, their current condition, treatment, length of stay,their home life and other risk factors. An AI based risk assessment tool canhelp prevent readmission, saving costs and improving quality of treatment.

Why CafeBot – CafeBot’s aim is to leverage AI in accurately predicting patients who are at risk before they are discharged, it will automatically engineer and identify the individual reasons for why each patient will be readmitted. It empowers data science teams to scale by dramatically increasing the speed to develop highly accurate predictive models with lesser number of resources. CafeBot includes innovative features including data ingestion from different data sources, data blending, data visualization, automatic machine learning and deep learning, model deployment and predictions, and interpreting the machine learning model built.

Problem/Challenge – Sepsis is a life-threatening condition that can occur in surgery patients. Around 80% of sepsis deaths could be prevented with rapid diagnosis and treatment according to Johns Hopkins Armstrong Institute for Patient Safety and Quality. Detecting sepsis can be really difficult because its signs and symptoms can be caused by other disorders.

Resolution – Machine learning algorithms can be leveragedto detect patterns in the data in all patients at the same time to detect risk of sepsis. An early diagnosis using machine learning algorithms can be done on routine vital signs and metabolic levels from electronic medical records can highlight patients at risk for Sepsis before they are admitted to the ICU.An AI based risk assessment tool can help prevent aggressive and more costly treatments which would therein improve patient outcomes.

Why CafeBot – CafeBot’s aim is to leverage AI in accurately predicting patients who are at risk of getting sepsis before their conditiongets matured, it will automatically engineer and identify the individual reasons for why each patient will be at a risk. It empowers data science teams to scale by dramatically increasing the speed to develop highly accurate predictive models with lesser number of resources. CafeBot includes innovative features including data ingestion from different data sources, data blending, data visualization, automatic machine learning and deep learning, model deployment and predictions, and interpreting the machine learning model built.

Problem/Challenge – Sending multiple or irrelevant product offers to a customer can make the customers inundated. Most of the time the emails or messages are generic or consist of products or offers which don’t meet the customer’s needs.Marketers need a way to determine when and how to contact customers so that they avoid spamming and improve product offerings.

Resolution – Machine learning algorithms can be utilized bymarketers so that they can determine which customers are likely to be interested in current offers and only those customers will receive them.With machine learning models marketers canidentify patternsbased on demographics, household data,past purchase behavior of the customers.An AI based recommendation tool can help increase customer satisfaction, higher brand value, and therein result in more sales.

Why CafeBot – CafeBot’s aim is to leverage AI in accurately predicting customer’s product of interest, it will automatically engineer and identify the individual reasons for why each customer has interest in that product or offer. It empowers data science teams to scale by dramatically increasing the speed to develop highly accurate predictive models with lesser number of resources. CafeBot includes innovative features including data ingestion from different data sources, data blending, data visualization, automatic machine learning and deep learning, model deployment and predictions, and interpreting the machine learning model built.

Problem/Challenge – Consumers these days increasingly expect relevant information provided to them. Marketer’s biggest challenges these days is relevance. For many marketers, broad segmentation based on historical data and segment-based content creation has been the best approach availablebut it is not practical or scale able for human teams to understand and adapt to the individual preferences of millions of customers.

Resolution – Machine learning algorithms canaccurately discover the preferences and purchasing behaviors of individual consumers. With machine learning models marketers can identify patterns about customers intent including real-time behaviors, prior purchases, preferences, and interests of similar customers.For example, on web pages, AI can be used to dynamically select the content including images and messages that will be most likely to convert a given customer.An AI based recommendation tool can help increase customer satisfaction, higher brand value, and therein result in more sales.

Why CafeBot – CafeBot’s aim is to leverage AI in accurately predicting customer’s interest aligned content, it will automatically engineer and identify the individual reasons for why each customer has interest in that product or offer. It empowers data science teams to scale by dramatically increasing the speed to develop highly accurate predictive models with lesser number of resources. CafeBot includes innovative features including data ingestion from different data sources, data blending, data visualization, automatic machine learning and deep learning, model deployment and predictions, and interpreting the machine learning model built.

Problem/Challenge – The retail and P&C industry is always growing which is therein resulting in low cost risk, decreasing store footprints, low profits etc.Markdowns for many retailers are driven by tried and rudimentary techniques with x% at 6 weeks, y% at 8 weeks etc.The lowest cost may win the business, but may be underpriced relative to the risk. This results in costing a company potentially exorbitant amounts of money in the end.

Resolution – Machine learning algorithms canaccurately discover patternsthat explore cost versus risk, you can determine whether any risk you consider taking on is priced appropriately.With machine learning models one can identify best price for each item using data on seasonality and price elasticity along with real-time inputs on inventory levels and competitive products and pricesAn AI based pricing tool can help push adverse selection on to competitors, which, over time will increase your growth and profitability.

Why CafeBot – CafeBot’s aim is to leverage AI in accurately predicting pricing/sales to maximize profits for various businesses, it will automatically engineer and identify the individual reasons for why the predicted price/sales value should be taken into consideration. It empowers data science teams to scale by dramatically increasing the speed to develop highly accurate predictive models with lesser number of resources. CafeBot includes innovative features including data ingestion from different data sources, data blending, data visualization, automatic machine learning and deep learning, model deployment and predictions, and interpreting the machine learning model built.