会社のデータをAIと統合し、保険金請求の状況を改善する方法について
人工知能（AI）は、これまで障害となっていた問題を各種分析へと分解し、保険会社がそのオペレーションを改善する手助けをします。最新のAIは、様々なフォーマットや構造のデータを処理し、分析することが可能であり、かつては想像できないと思われていたインサイトを提供します。データ品質の懸念にも関わらず、保険金部門には豊富なデータがあり、AIはその可能性を解き放つカギになります。
理想的世界では、保険金請求データは最初の損失の連絡から支払いの終了まで、正確で、完全で、迅速に報告されるはずです。しかし実際には、損害査定担当者はしばしば様々な遅延や、不完全、不正確なデータに頻繁に遭遇します。データがシステムにある場合でも、データにアクセスして分析し、重要なパターンを特定することが難しいこともあります。不整合なコーディング、欠落データ、不正確性などのデータ品質に関する懸念が、データ分析能力の更なる妨げとなります。多くの保険会社が、自社のデータにはAIが必要とする完全性が欠如していると誤解しています。
自社のデータに疑問があるなら、プレディクティブ分析に注力してみてはいかがでしょうか。
AIの進化は、保険金請求のプレディクティブ分析をより利用可能で実現可能なものにしてきました。保険金請求データの弱点は、現在のAI技術のフレキシビリティや機動性で克服することが可能です。
AI技術の適応力は、空欄や不正確なデータがある場合でも、様々なフォーマットの保険金請求データを利用可能にします。 保険アナリティクスとAIは、今や、全てではなくとも大部分の、保険金請求データの弱点に対応可能であり、保険金請求実績およびトレンドに関する貴重なインサイトを提供します。
A break with the past
Unlike regression models that rely heavily on structured data, the best predictive models today deploy a variety of statistical and analytical techniques that are used to identify patterns and trends. The most sophisticated models use natural process language, a branch of AI, which extracts meaning from text such as adjusters’ notes and other word documents.
This ability to read or text mine unstructured data (such as claim descriptions, adjuster notes, or any other free-form text data) provides a rich view of the claim information, allowing claims professionals to understand what is happening on a claim before costs become problematic. Claims with characteristics like co-morbidities or upcoming procedures that have the greatest influence on costs can be identified early in the life cycle of a claim, and directed to seasoned adjusters who can bring in cost containment resources early in the process when they’re most effective. Similarly, the least costly claims can also be identified, fast tracked and closed, freeing up claims’ resources for more complicated cases. This claims triage process allows claims to be efficiently moved to the most appropriate resources. Much of the time-consuming, manual review that often goes into claims assignments is replaced by a process based on quantitative factors, which greatly increases claims automation. Increased automation leads to increased efficiency and fewer manual errors.
Moreover, the unstructured data accessed by AI is often more informative than the structured data. Static in nature, structured data typically gives a snapshot of injuries and fails to capture their evolving nature. Radiating shoulder pain that occurs after an injury was coded, reduced mobility that develops weeks after an accident, or a discussion of possible surgery. These and other latent characteristics of a claim, which reflect the true medical complexities of an accident, are not reflected in structured data but are instead buried in adjusters’ notes or other text documents. AI now provides access to this data and a pathway for insurers to benefit from claims analytics and insight into the costs that are driving the potentially most expensive claims.
A rich view of the data also brings a new dimension of granularity to the data that can reveal subtle, but cogent, shifts in insurance claims trends as they begin to emerge. Claims professionals can see if key indicators are changing when they need to review claims performance, literally at the press of a button, instead of waiting until the end of the month or another arbitrary time that is often beyond their control.
This improved granularity means that a claims professional can gain a sharper understanding of the specific factors that affect his or her company’s claims performance rather than relying on industry trends that may or may not reflect an individual insurer’s claims profile. The need to react to changes can be replaced by the ability to proactively adjust to shifts in trends and thoughtfully intervene as changes start to emerge.
Over time, claims predictive analytics can help to improve an insurer’s overall data quality, since the unstructured data accessed by AI is also used to verify an insurer’s structured data by identifying data that seems out of sync with known trends. This is often observed, for example, in the low rates of obesity in insurers’ claims profiles compared with known rates reported in the U.S. population. The best predictive models will also normalize differences in the way adjusters report data based on the insurer’s unstructured data. This process of investigating and reconciling these differences and anomalies in the data will also reveal ways in which an insurer’s data collection process can be improved. Over time this reiterative loop between structured and unstructured data not only improves the overall quality of an insurer’s data but also the predictive capabilities of the claims model.
Conclusion
Data that many insurers were unaware they had can now provide a gateway to claims predictive analytics that brings new insight to claims performance and bolsters insurers’ ability to compete in an ever increasingly data-driven market. Embracing the technology is simply no longer the risk that it was, no matter what an insurer’s data quality perceived concerns may be. But to truly leverage the hidden intelligence in claims data, insurers need a trusted partner with deep subject matter expertise in data science, domain knowledge of the insurance industry and the ability to keep pace with rapidly changing technologies.
This expertise has been brought together in Milliman NodalTM, the analytics platform specifically built for the insurance industry and designed with actuarial science and claims operations expertise as an end-to-end solution for insurers. Companies that have deployed Nodal reported an average cost savings between 5% and 15%.