Kumo Co-Founder Hema Raghavan Named to Inc.’s 2026 Female Founders 500
Industry
Claims fraud costs the insurance industry $80B+ every year. Traditional ML makes it worse by flattening relational data into feature tables, destroying the signals that actually predict claims fraud, underwriting risk, and policyholder retention. KumoRFM learns directly from the relationships in your data and is pre-trained on tens of thousands of datasets, delivering higher accuracy than any internally-built model, in hours, not months.
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Why insurers choose Kumo
Here's how Kumo transforms insurance operations with relational AI.
45%
More fraudulent claims caught
KumoRFM traces relationships between claimants, providers, policies, and historical patterns. It detects coordinated fraud rings and staging patterns that rules-based systems miss entirely.
28%
Better loss ratio predictions
Pre-trained on thousands of relational schemas, KumoRFM understands risk patterns across policyholder demographics, claims history, and coverage interactions your actuarial models cannot capture.
Hours
Not months to deploy new models
Claims fraud, underwriting risk, retention scoring, cross-sell propensity. Deploy all of them from one platform without building custom feature stores for each line of business.
Loved by data scientists, ML engineers & CXOs at
One platform, every prediction
From claims fraud to policyholder retention, Kumo learns from your connected insurance data to power every prediction your actuarial and data science teams need.
Identify fraudulent claims by learning patterns across claimants, providers, policies, and historical claims — catching organized fraud rings that rule-based systems miss.
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Predict loss probability more accurately by learning from the full relational context of applicants, prior claims, geographic risk factors, and coverage history.
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Set premiums that reflect true risk by modeling the relationships between policyholder attributes, claims patterns, and market conditions — improving loss ratios while staying competitive.
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Identify policyholders at risk of non-renewal by learning from interaction history, claims experience, pricing changes, and competitive signals across your book of business.
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Predict which policyholders are most likely to purchase additional coverage by learning from product affinity patterns, life events, and relational signals across your customer base.
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Identify claims with high subrogation recovery potential by analyzing relationships between claim characteristics, third-party involvement, and historical recovery outcomes.
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Improve reserve accuracy by learning from the relational patterns between claim severity, development trajectories, claimant behavior, and adjuster assessments.
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Forecast agent production and retention by modeling relationships between agent attributes, book composition, market conditions, and historical performance trajectories.
Route and prioritize new claims instantly by learning from historical patterns of claim complexity, fraud likelihood, and resolution pathways across your claims data.
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The insurance data advantage
Feature engineering destroys signal. Even with a world-class data science team, the traditional ML approach forces you to flatten 10, 20, 50 relational tables into feature vectors. When you do that, you discard the nuanced relationships between policyholders, claims, adjusters, providers, agents, and billing history. A fraud ring spanning three clinics and twelve claimants becomes a row of aggregated counts. This isn't a talent problem — it's a structural limitation of the approach itself.
You only have your data. Even the best internal model is trained on one carrier's data. KumoRFM is pre-trained on thousands of relational schemas across industries. It has already learned what patterns look like across hundreds of different data structures — the same advantage GPT has over a custom NLP model. Your team, no matter how talented, cannot replicate foundation-model scale.
Your existing team will love it. KumoRFM doesn't replace your data scientists — it 10x's them. Instead of spending months on feature engineering and pipeline work, they define predictions in a simple query language. They go from shipping 3–5 models per year to 50+ per quarter. The tedious work disappears; the interesting work remains.
One platform powers claims fraud detection, underwriting risk, pricing optimization, churn prediction, and every other insurance prediction — from the same connected data, with the same team, at 10x the velocity.
95%
Less data preparation
Automated feature engineering
30–40%
Claims fraud reduction
Over traditional rule-based systems
20x
Faster to production
From months to hours
$80B+
Annual insurance fraud
The problem Kumo helps solve
KumoRFM isn't limited to your data alone. Pre-trained on billions of relational patterns across diverse datasets and fine-tuned to your schema, it sees what no in-house model can. As per the SAP SALT benchmark.
LLM
GPT4 + AutoML
PhD Data Scientist
Feature eng. + XGBoost
KumoRFM
Relational Foundation Model
40%
lift in prediction accuracy
“Beating internal XGBoost model on key metrics with far less data/features — on Kumo pre-trained. We replaced six months of pipeline work with a single afternoon.”
Matt Loskamp
GTM Data Science Leader, Enterprise Financial Customer
From startups to enterprises, leading organizations rely on Kumo to deliver predictive insights at scale.
Peer-reviewed
Kumo is built on 40+ peer-reviewed papers at NeurIPS, ICML, and KDD. The methodology is public and reproducible.
K. Huang, M. Fey, J. Leskovec et al.
A foundation model for relational data - pre-trained across schemas, it delivers accurate predictions out of the box and improves with fine-tuning on your specific data.
Read paperM. Fey, W. Hu, K. Huang, J. Leskovec et al.
Introduces learning predictive models directly on relational databases, eliminating the feature engineering pipeline that has historically bottlenecked enterprise ML.
Read paperJ. Robinson, R. Miao, K. Huang et al.
An open benchmark for evaluating relational prediction methods across 11 databases and 30 tasks. Kumo consistently outperforms traditional ML baselines.
Read paper
