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RiverStar Software, Inc. — Low-income household energy-assistance case records for the federal home energy assistance programme, captured end to end by community care agencies: applicant screening and eligibility-determination responses (household size, income, fuel/heating type, tenure), the utility-bill and arrears amounts submitted as proof, benefit approval and grant amount, vendor/utility payment records, denial and ineligibility reasons, fraud-detection flags, appointment and reminder interactions (text/email), language of application, channel used (phone agent versus self-service deskt…
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Low-income household energy-assistance case records for the federal home energy assistance programme, captured end to end by community care agencies: applicant screening and eligibility-determination responses (household size, income, fuel/heating type, tenure), the utility-bill and arrears amounts submitted as proof, benefit approval and grant amount, vendor/utility payment records, denial and ineligibility reasons, fraud-detection flags, appointment and reminder interactions (text/email), language of application, channel used (phone agent versus self-service desktop/mobile), and a cross-agency deduplication index that identifies the same household applying at multiple organisations
From 10 yearsCoverage Information Technology · UtilitiesAsset class Equities · Fixed income
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RiverStar Software, Inc. — Low-income household energy-assistance case records for the federal home energy assistance programme, captured end to end by community care agencies: applicant screening and eligibility-determination responses (household size, income, fuel/heating type, tenure), the utility-bill and arrears amounts submitted as proof, benefit approval and grant amount, vendor/utility payment records, denial and ineligibility reasons, fraud-detection flags, appointment and reminder interactions (text/email), language of application, channel used (phone agent versus self-service deskt…
Riverstar offers (Alternative, ESG, Reference, Consumer) — Estimate hundreds of thousands to a few million complete assistance cases to date (e.g. one 8,000-case-per-year agency multiplied across a tenant base of dozens of agencies over a decade of seasons), each case carrying income, bill, fuel-type, determination-outcome and payment fields — a comparatively small row count whose value is field depth and the longitudinal repeat-applicant history, not scale.
Energy-poverty and energy-burden measurement with actual measured bills and income rather than expenditure-surrogate estimates (the federal statistics on this are sample-survey based and lag years); validation and recalibration of federal low-income energy models and threshold assumptions; predictive modelling of assistance demand by geography ahead of winter for agency budgeting and crisis-fund allocation; targeting models for weatherisation and efficiency retrofits, since high-bill-plus-low-income is the highest-yield retrofit signature; fuel-transition and electrification equity analysis from the heating-fuel-type field across low-income cohorts; credit-shutoff and utility-arrears risk modelling for consumer-lending and utility collections; fraud-detection model training on labelled cases; training data for conversational intake and eligibility-prediction assistants
The data is with 10 years of history.
Coverage spans US; Application Software, Electric Utilities; alternative, ESG, reference, consumer; equities, fixed_income.
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