Gigaforce Launches Game-Changing Claims Intake Bot with Hyper-Automation Capabilities

Insurtech company delivers technology advancements for a near-touchless claims experience across boundaries


SUNNYVALE, Calif., June 21, 2022 (GLOBE NEWSWIRE) -- Gigaforce, an InsurTech provider of a SaaS-based, blockchain-optimized, claims platform, is pleased to announce the launch of a new claims intake bot, Gigabot, which moves the company’s clients significantly closer to a touchless claims experience.

“While most people think of chatbots handling customer service, Gigabot does the actual heavy lifting by enabling end-to-end claims automation and significant cost savings for insurance companies and the partners in their ecosystems,” said Sanjeev Chaudhry, CEO of Gigaforce. “This product launch is directly in line with our mission to streamline the insurance claims process while substantially increasing efficiency and accuracy by better leveraging the data that moves in and out of insurance companies daily.”

Gigabot provides substantial savings in both time and cost by extracting claim data and documents from the originating software and placing it on the Gigaforce Platform. While streamlining claims intake, Gigabot also makes real-time data and transactional updates more transparent to the insurer/reinsurer, recovery firm, and any other partners collaborating to manage and settle claims.

As part of the Gigaforce Platform, the first and only distributed ledger technology (DLT) platform for full claims automation available in insurance today, Gigabot comes with critical hyper-automation features, including:

  • Automated File Redaction: After Gigabot retrieves the claim files, sensitive information, such as personal health information (PHI) and personally identifiable information (PII), contained in the files is redacted through robotic process automation (RPA) and machine learning (ML). The Gigaforce Platform leverages sophisticated redaction capabilities, including optical character recognition (OCR) for unstructured digital content or direct processing with natural language processing (NLP) data.
  • File Indexing: Gigaforce logically splits documents and other unstructured data based on embedded values, with machine learning for OCR and data extraction. The platform can create and maintain distinct files, with different personnel assigned to each file. Artificial intelligence (AI) is utilized to sort files into categories and/or subcategories, and documents are automatically split so data appears in the appropriate area. Human intervention is needed only for governance to confirm files have been successfully managed by the system.
  • Subrogation Scoring: The Gigaforce Platform can identify subrogation opportunities and subsequently provide a score for recovery potential.

“We have seen lightning speed acceleration of the digital transformation journey in insurance over the past two years,” said Chaudhry. “Insurance companies and all who do business with them stand to realize immediate benefits and a fast ROI when deploying this technology. In medical claims alone, the AMA estimates a $12B annual decrease in the cost of settling claims through increased automation. On average, Gigaforce customers have reduced processing time by 20 percent, increased customer retention, and realized a nearly 25 percent decrease in the cost of settling claims. To remain viable for the long term, hyper-automation is imperative to customer retention. Settling claims in days or weeks as opposed to months will be critical, for example. We’ve entered the digital age of insurance and there’s no turning back.”

About Gigaforce
Gigaforce Inc. is an InsurTech provider of a SaaS-based, blockchain-optimized, claims platform which combines artificial intelligence (AI)-driven predictive models, specialized expertise, and customized services to expedite subrogation, recovery, and salvage processing for insurance ecosystems, including insurers, law firms, and third-party adjusters (TPAs). For more information, please visit www.Gigaforce.io.

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Jennifer Overhulse
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