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複雑な意思決定の調整を担当するATLSの認知センター。
Copyright © 2024 Lazarus Enterprises, Inc.
All rights reserved.
私たちのモデルは、与えられたインプットに基づいており、実証すべき信頼できるデータが見つかった場合にのみ応答するように調整されます。
私たちのプロセスには「ブラックボックス」はありません。アウトプットはすべて、説明可能性を示す指標と監査可能なコンテキストを提供します。
当社のソリューションはお客様のファイアウォールの内側にも導入できます。あなたが送るデータとそのデータを使って何をするかは、あなただけのビジネスです。
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リカイ2の機能
リスク引受け
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「メリットをまとめるのに3日ほどかかりました。それ以来触る必要はありませんでした。(12か月以上稼働しています)」
—CTO
保険仲介
「約 80% のケースで、当社のクレーム処理担当者は、医師が作成した要約よりもお客様の要約を優先していました。（残りの時間はほとんど同点でした）。
—最高情報責任者
生命保険会社トップ5
「プロンプトの最適化を試みることなく、3日間かけてプロンプトを作成したことで、通常は3か月かかっていたエンジニアリング作業を6週間削減できました。」
—シニアデータサイエンティスト
大型労働者災害補償運送会社
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データの抽出と分類を8週間で正確に自動化
M&A取引におけるポートフォリオ全体のリスク評価を含む高度なレビューにより、ROIが最大10倍に
障害請求のエンドツーエンド処理を 2 ～ 3 週間から 30 分に短縮
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October 11, 2024
In partnership with our customer, a leading US Insurance company, we engineered an AI first data processing workflow by automating data extraction and signature verification. With over 97% accuracy and confidence levels, this solution allows staff to focus on high-value tasks, showcasing the transformative power of AI in the insurance industry.
もっと読むOctober 11, 2024
Discover how Lazarus AI accelerated claims processing for a leading US Life Insurance company by reducing time-sensitive claim handling from 30 days to just 30 minutes. By seamlessly integrating advanced AI technology into existing systems, Lazarus AI enhanced accuracy and efficiency, mitigating reputational and regulatory risks while improving customer satisfaction.
もっと読むOctober 11, 2024
A reinsurance company leveraged Lazarus AI to identify critical data anomalies during a potential acquisition. By integrating seamlessly with existing tools, Lazarus AI revealed significant under-reserving issues, saving the company from unforeseen liabilities. This case study highlights the power of AI in enhancing due diligence and ensuring data quality, providing a competitive edge in the insurance industry.
もっと読むOctober 11, 2024
Our team helped redesign underwriting for a top US life insurer by leveraging advanced AI models to refine risk assessment. By identifying nuanced patterns in medical data, the insurer was able to accurately price premiums and approve previously rejected applications, enhancing profitability and underwriting quality.
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The difference between generative AI and extractive AI is straightforward. Yet, Lazarus AI still sees these approaches confused on a daily basis. This confusion prevents companies from getting real value out of AI and creates misconceptions about the viability of enterprise AI solutions. Both approaches are powerful and both have uses in the modern corporation. Misusing one of these approaches is like misusing any other tool: it can lead to endless frustrations and inefficiencies.
もっと読むThe decision between a point solution and a platform depends on your organization’s specific needs, budget constraints, and long-term vision. Assess the trade-offs in terms of functionality, ease of implementation, scalability, and integration capabilities to make the most appropriate choice for your document understanding needs.
もっと読むThis insight presents a perspective on the concept of Explainability in AI. This topic will be of intense interest while both State and Federal authorities work through the rules of the road. In the interim, insurers need to strive for explainability and hold their partners accountable.
もっと読むThis Insight presents five steps needed for success in the world of prompting. As noted earlier, prompting is both art and science and prompting will continue to evolve quickly. Following the Steps and guidance here will maximize probability of success and ignoring the guidance here will increase risk, money, and time. Continue to watch Lazarus for Insights as this evolution occurs.
もっと読むEffective prompting is a vital component of implementing LLM solutions in the insurance industry. To keep up with the current state of AI technology, insurers should look to develop their prompt engineering capabilities in 2024. Knowledge workers across all domains will need to learn prompting skills to effectively use LLM-based tools (general prompting). Dedicated prompt engineering professionals will not disappear, rather their responsibilities will shift towards large-scale and specialized prompting tasks (enterprise prompting).
もっと読むThis Insight leverages Lazarus AI’s experience in the insurance industry to present a simple framework for conducting an effective POC. Many insurers have successfully completed a POC and implemented AI technology in production. In the coming year, many more will. We at Lazarus AI are available to help you whether you are just starting to develop use cases or are ready to dive into a POC of your own.
もっと読む
