Kumo Co-Founder Hema Raghavan Named to Inc.’s 2026 Female Founders 500
Industry
Fraud losses are accelerating, credit defaults are rising, and AML costs are exploding. Traditional ML makes it worse by flattening relational data into feature tables, destroying the signals that actually predict fraud, default risk, and money laundering. 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 financial institutions choose Kumo
Here's how Kumo transforms financial services operations with relational AI.
40%
More fraud caught at same false-positive rate
KumoRFM traces transaction chains, account relationships, and behavioral patterns across your entire graph. It detects coordinated fraud rings that rule-based systems and flat-table models structurally miss.
3.2x
More accurate credit risk scoring
Pre-trained on thousands of relational datasets, KumoRFM understands financial patterns your internal data alone cannot teach. The result: dramatically better risk stratification with fewer defaults.
50+
Models per quarter from the same team
Fraud, AML, credit risk, cross-sell, churn, collections prioritization. One platform replaces dozens of custom pipelines, each shipping in hours instead of months.
Loved by data scientists, ML engineers & CXOs at
One platform, every prediction
Financial services prediction problems share the same underlying data. Kumo learns from all of it simultaneously — no per-model engineering, no per-use-case pipelines.
Kumo learns from the full transaction graph — amounts, timing, merchant categories, device fingerprints — detecting fraud patterns that rule-based systems miss entirely.
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Predict default probability from the relational structure of accounts, transactions, payment history, and credit bureau data — not just static credit scores.
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Detect laundering patterns by learning from transaction flows, counterparty relationships, and behavioral anomalies across the entire account graph.
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Identify at-risk banking customers from declining engagement, reduced transaction volume, and competitor product signals weeks before they leave.
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Predict which financial product each customer is most likely to adopt next based on their full relationship history, life events, and peer behavior.
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Identify suspicious insurance claims by learning from claimant history, provider relationships, and cross-claim patterns across the network.
Detect compromised accounts from login anomalies, device changes, and behavioral shifts — before unauthorized transactions occur.
Predict individual-level LTV from product holdings, transaction patterns, service interactions, and relationship depth across all accounts.
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Automate risk-based customer due diligence by learning from entity relationships, transaction patterns, and adverse media signals.
The financial data advantage
Even if you have a world-class data science team, feature engineering fundamentally limits their accuracy. When you flatten 10, 20, or 50 relational tables into feature vectors, you discard the nuanced relationships between entities. The connections between accounts, transactions, counterparties, devices, and merchants encode fraud rings, credit risk patterns, and suspicious activity. Flattening those into a single row loses most of that signal. This isn't a team quality problem — it's a structural limitation of the traditional ML approach.
Even the best internal model is trained on one institution's data. KumoRFM is pre-trained on thousands of relational schemas across industries. It has already learned what “fraud looks like” and “default risk looks like” across hundreds of different data structures. Your team, no matter how talented, cannot replicate this breadth. This is the same advantage GPT has over a custom NLP model — foundation model scale.
KumoRFM doesn't replace your data science team — 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 — defining what to predict, interpreting results, driving business impact — remains.
One platform powers fraud detection, credit risk, AML, cross-sell, and every other financial prediction — with higher accuracy, in a fraction of the time, from the same connected data.
95%
Less data preparation
Automated feature engineering
15–25%
Net fraud loss reduction
Over traditional rule-based systems
20x
Faster to production
From months to hours
$485B
Global fraud losses annually
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
