The data with minimal latency in Data Science ecosystem in Finance, institutions are able to track transactions, credit scores and other financial attributes without any issue of latency.
Real time analytics is the analysis of data as soon as that data becomes available. In other words, users get insights or can draw conclusions immediately (or very rapidly after) the data enters their system. Real-time analytics allows businesses to react without delay.
Risk analytics a data driven analytical platform , a company is able to take strategic decisions, increase trustworthiness and security of the company. Since risk management measures the frequency of loss and multiplies it with the gravity of damage, data forms the core of it.
Risk analysis is a technique used to identify and assess factors that may jeopardize the success of a project or achieving a goal.
Predictive Analytics in healthcare
Predictive Analytics is playing an important role in improving patient care, chronic disease management and increasing the efficiency of supply chains and pharmaceutical logistics. It is a data-driven approach focusing on prevention of diseases that are commonly prevalent in society.
Predictive analytics is the process of learning from historical data in order to make predictions about the future (or any unknown). For health care, predictive analytics will enable the best decisions to be made, allowing for care to be personalized to each individual.
Chatbots : Too hyped or right success
Chat bots is one of the cool topics around but sometime I wonder whether is it too hyped or a rightly implemented proven success to business? any thoughts
The most powerful chatbots — and the ones that can actually make an impact on customers' experience and company bottom lines — are virtual agents. ... Like all successful automation efforts, customers service chatbots can reduce costs, but their true value lies in the improvement they bring to the customer's experience.
Insurance claim Analytics
Insurance Claims analytics , the process of paperless and touch-less claims process , which does not require any kind of human intervention. As in current process the claims go through multiple employees which may affect the quality of the process. Insurance claim analytics can be a solution by method of automated reporting, capturing, auditing and communication.
The ability to use insurance claim data analytics through machine learning can improve insurance and claims companies bottom line and overall profit.
InsurTech is one of the most demanding and growing field of data analytics , by the use of Insurance Underwriting Analytics , which provides detailed solutions for prudent Underwriting for better analyze of specific risk associated with new policies request or existing customer before taking any further decision.
Insurance underwriters are professionals who evaluate and analyze the risks involved in insuring people and assets. Insurance underwriters establish pricing for accepted insurable risks. The term underwriting means receiving remuneration for the willingness to pay a potential risk.
Data analytics is Insurance savior
Insurance companies are struggling to come out from ill effect of bogus claim from many years. Big Data based fraud detection tool with the principle of forensic science can be one of the solution for elimination of bogus claim from the system. The data analytics based system can be used both general and life insurance field.
Data analytics in the insurance industry is transforming the way insurance businesses operates. ... Insurers are relying heavily on big data as the number of insurance policyholders also grow. Big data analytics canhelp solve a lot of data issues that insurance companies face, but the process is a bit daunting.
Retail chains - Starbucks - Data analytics and Business Intelligence
Have you ever wondered how your favorite coffee shop uses data analytics and business intelligence (i.e. BI) to deliver to you the unique Starbucks Experience? This largest and the most recognizable coffee brand is one of the places where business and data analytics solutions meet in the real world.
Any new update in the field , as I am working to develop some web based platform for coffee producers of south India along with a group of my student .
Significance of Predictive Data Analytics in Banking
Banking industries are rich with data. Used or unused, there is an excess amount of data in these sectors. Most banks are under pressure to stay profitable and simultaneously understand the needs, wants and preferences of the customers. Lately, many financial institutions have adopted new models that help them to compete. Banks need to go beyond their standard business reporting and sales forecasting to be able to identify a set of crucial factors relating to success.
The application of data mining and predictive analytics to extract actionable insights and quantifiable predictions can help the banks to gain insights that comprise of all types of customer behavior, including channel transactions, account opening and closing, default, fraud, and customer departure.
Multi Dimensional Visualization
This visualization technique for displaying multiple dimensions has been developed by a research group at the University of Athens. They are looking for feedback from colleagues in the field on its functionality and utility.
Multidimensional data visualization represents one dimension as a point, two dimensions as a two-dimentional object or graph, three dimensions as a three-dimensional object or graph, and four or more dimensions as a movie, or a series of three-dimensional objects of graphs.