Optimizing claims: a Big opportunity for Big Data Advanced Analytics
Optimizing claims: a Big opportunity for Big Data Advanced Analytics
Mon 08 Aug 2016
Thanks to Advanced Analytics, insurance providers can bring claims management into a new era by combining automatic processing of large volumes of data with human expertise.
Analyzing data in order to optimize the claims processing chain is not a new phenomenon for insurance providers, notably in the field of health care. The health care sector is actually one of the very first to have adopted automated data processing. Given the thousands of cases that must be processed daily, insurance providers or mutual health organizations are not only players in the health care industry, but are also data operators with an obligation to deliver results within set deadlines. Computerized environments have proven to be essential as they help speed up these processes. However, given the rapidly increasing volume and complexity of cases, these systems lack flexibility, resulting in high costs while also presenting claims processing quality problems. By combining the best of both worlds (automated processing and collaborative analysis between experts), we are providing a concrete daily solution for the largest German insurance providers and American health care centers.
What’s at stake in optimizing claims?
Three major side benefits on top of qualitative productivity: financial, regulatory and customer satisfaction.
Generally speaking, insurance providers manage two types of claims: high impact and regular impact. Unfortunately, the majority of cases are a mixture of both, which increases the complexity of claims processing and renders automated systems ineffective. Software solutions are generally incapable of detecting such cases or managing processes with ramifications in various domains, which is characteristic of these types of cases. Due to a lack of specialized training, consultants implementing traditional systems are no better equipped to adjust processing for complex cases.
In many instances, the quality of case processing is inversely proportionate to their amount, and the number of complaints is constantly increasing. Required to intervene manually, experts have difficulty managing their daily workload, especially when they must also process complaints due to claims being improperly managed by automated systems. This mode of functioning naturally causes delays in health care payments, which is detrimental for both the insured and the insurance provider: the risk of exceeding regulatory deadlines is not insignificant, as infractions lead to interest being charged, as well as late fees. Furthermore, such erroneous or overly long processing inevitably leads to dissatisfied customers.
However, claims management remains a key part of the relationship between an insurance provider and the insured. Firstly, insurance providers can optimize and maximize their knowledge, as well as assess legislative impacts and detect deviant behaviour. Secondly, the quality of responses they provide, as well as their ability to provide information about a claim’s progress represent a considerable advantage in optimizing customer relations.
From a purely financial point of view, the challenge of optimizing claims processing is twofold for insurance providers. They must first detect abnormal and/or fraudulent behaviour, and also reduce their costs by identifying the most profitable service providers (compensation) and the costliest types of claims.cl
Considering all of these issues, it is understandable that the management of claims cases is of utmost importance. A task which is already difficult due to the complexity of cases is made even more difficult by the proliferation of data, the daily volume of cases to process and the need to use a process that is reliable and complies with regulatory requirements.
What is possible today that was not before?
Although traditional solutions automate and simplify process management, they are incredibly inflexible: depending on the problem, one or several products is required to manage a process from end to end. This way of assembling “stacked” software is problematic: it requires technical integration and does not promote “intelligent” collaboration on a human scale. These expensive methods are also synonymous with underutilizing products and limited operating lives, which generally results in users experiencing difficulty adapting to new environments, a loss of time and the need for training sessions, which are all costly as well. These traditional environments, which have been reinforced over the past several years due to the use of “patches” to ensure the consistency of integrations, make processing reliable and facilitate the work of operational actors, are far from being a cure-all, especially as they are also overly “predictable” and therefore are more easily exploited by scammers.
In reality, traditional solutions simply no longer meet today’s challenges, as the “data” component has become too central. No common treatment of high impact and regular impact cases in traditional analysis environments is available. Yet Insurance Companies are in need of common treatment for both.
Today more than ever they need reactive depth and agility achieved by using systems in which automation allows large volumes of data to be managed, while facilitating the cross-checking of internal and external sources in order to perform relevant analyses with a granularity of information that is tailored to their needs. Analytics application has to be adapted to all requests dynamically which cannot be achieved in a stacked architecture. Ours is suited for all requests at once. This kind of raw format allows to fast answer predictive and prescriptive questions.
What about the promise of more “intelligent” automated processing?
This is precisely the challenge that Advanced Analytics seeks to overcome, more specifically what we dedicate ourselves to at Panoratio. Derived from the original promise of Big Data created by McKinsey just five years ago, Advanced Analytics adds both a predictive and prescriptive dimension. It allows insurance providers and mutual health organizations to truly benefit from automation with a low to non-existent margin of error. Advanced Analytics allows environments to be implemented that are able to process massive volumes of data, with automated processes for simple cases and semi-automated processes for cases requiring expertise or human arbitration. As they are able to go much further in their analysis, they allow the strategic and dynamic dimensions of data to be understood in order to address the main challenges of insurance providers and encourage the implementation of operational control systems which are optimized for the context of claims case management. In addition, it offers a work environment that is conducive to collaboration in which operational actors are able to share detailed views on identified cases. These views have been specially designed to be especially simple and easily understood by those in a particular business.
Is the solution based on operational arbitration or intelligent automation?
Neither completely computerized nor completely manual, the solution lies in striking a balance between man and machine. To help insurance providers achieve this balance and find their desired flexibility and level of information, assistance from expert consulting groups is essential. Backed by their vast experience and expertise, consultants are able to make sense of their need for quick adaptability and address their most complex and hard-hitting problems.
It is an holistic approach that not only allows insurance companies to expand their business processes and keep pace with the information society, but also allows them to adopt a proactive approach by testing out various scenarios to better understand the changing population behaviour.
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