Energy Utility Analytics Market. Energy Utility Analytics Market: Overview For encapsulation of advantageous opportunities in the energy sector, utility and energy companies are transforming their systems into smarter energy systems, which would feature a new-way flow of information in energy and utility sectors.
Energy utility analytics enables near real-time analysis of processes, thereby helping in optimization of operations through efficient identification and isolation of inefficiencies and failures. The energy utility analytics market has the following …. Predictive Analytics Market -. The global predictive analytics market is categorized on basis of end-users, software solution types, applications and mode of delivery.
The market overview section of the report demonstrates the market dynamics and trends such as the drivers, restraints and opportunities that influence the current nature and future status of this field. Operational Analytics Market Growth Analytics Global Operational Analytics Market: Snapshot The global operational analytics market is prognosticated to showcase a high potential for growth in the forthcoming years on the back of decisive factors such as the dominating advent of Internet of things IoT -enabled devices.
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Sign Up. Like this presentation? Why not share! What is Big Data? Embed Size px. Start on. Show related SlideShares at end. WordPress Shortcode. Next SlideShares. Download Now Download to read offline and view in fullscreen. Insurers are now able to run predictive and entity analytics during multiple touch points, essentially as each new piece of information is added.
This not only improves detection capabilities in the event of fraud, but it also allows an insurer to assess a fraud-risk. Some have begun providing risky policy holders with high-priced policies in order to drive them to other service providers.
The insurer today has moved away from a purely reactionary stance to a proactive effort to keep bad business off of its books. Beyond this shift, much of current evolution is around communication and it presents a clear opportunity for moving forward.
While analytics engines may get much of the coverage, the successful fraud detection unit of tomorrow features a very well-educated staff. Fraud professionals are being asked to step up to the plate like never before.
They have access to more data and increasingly strong ways to manipulate it. Staff will need to be trained in these systems as well as new fraud tactics. Insurers want to automate the fraud process as much as possible to weed out as many proper claims and false positives as possible.
At the end of the day, however, any flagged accounts still must be reviewed by a person. A well-trained team can improve models by determining what normal behaviour is and what fraudulent behaviour is. Use of analytics for fraud detection in insurance is essential to the future viability of the market. However, there is no mad rush to adopt new third-party technologies or shift infrastructure.
Recent market events have made this image much clearer than many would have thought at the turn of The flaw going unnoticed for years has likely caused a major reduction in plans for insurers to move any part of operations to the cloud.
When the answer focused on reduced margins and increased competition, cloud-based analytics were an easier case to make. As austerity budgets continue in the UK and Europe, individuals, groups and gangs will look to the softest option to make ends meet. Multiple insurers said their industry can learn a lot from credit card fraud detection. These companies have adopted and invented new technologies to detect and deter fraud because of a compelling business reason to act: regulators look heavily at money laundering.
Third-party data may play a role in fraud detection but it will likely reside in systems run by the IFB, police, and other law enforcement for the near term. In the UK, customer data is very strictly monitored. Similar protections are in place in France and Germany, and EU nations are likely to move toward stricter data controls in the future. Many insurers and other industries still feel burned from outsourcing and offshoring their customer service to third-parties.
Fraud detection systems become worthless when errors are introduced, so there is little likelihood of complex systems being outsourced to anyone, even native developers.
The largest hurdle faced by insurers remains legislative barriers to sharing and pursuing information. Where legislation allows, insurers are poised to collect and analyse new data and deliver better results. The push toward Big Data and analytics for fraud is coming with a clarion call of automation and modelling.
Fraud detection still needs a human touch. While data is at the core of the current revolution in insurance industry practices and advances, it must inherently remain an industry that relies on gut feelings and human insight.
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