RenaAi 레나에이아이
KORENG

We build alternative data with our own methodology
and connect it to global financial markets

RenaAi collects and organizes data scattered across market areas, equities and patents, and uses validated models to turn it into indicators such as probability of default, leading signals and patent quality scores. We deliver them via API and data feed, ready for lenders, rating agencies and asset managers to use in underwriting and investment.

RenaAi data pipeline

Source data (market areas · equities · patents)

Market area × sector × quarterRegulatory filings (DART)Prices · trading flowsPatent claimsGlobal family
1NormalizationDisparate sources on one standard
2Signal separationFilter noise, keep leading signals
3IndicatorsPD · leading index · patent quality score
Connected to financial marketsREST API · data feed
Data partnership & PoC inquirySee our three datasets

Why alternative data

Some things are hard to assess from financial statements alone.

Financial statements record what has already happened. Business owners with no credit history, changes before they are filed, patents carried at zero — to see what financial statements miss, you need other source data.

A shop can do well and still be hard to assess without a credit history

Conventional credit assessment relies mainly on past loan and card payment history. The 3.16 million thin-file business owners have little data to be scored on, even when their shops run steadily. They are easily classed as high risk, and many are pushed into high-interest loans at 15–20% a year.

Local market areas · 3.16 million small businesses
Small businesses in the credit blind spot3.16M

Quarterly earnings filings record what happened months ago

Real change inside a company shows up in transactions, trading flows and on-the-ground data months before it appears in regulatory filings (DART). By the time decisions are made on filed financial statements, the opportunity is often gone and the risk is already priced in.

Capital markets · listed companies
Reporting lag of financial statements45–90 days

Core patents that cost millions of dollars are recorded at zero

Financial statements do not measure the real competitive strength of intangible assets. Even globally competitive technology patents are written off on the balance sheet as research expenses, so companies miss out on fair valuations and financing based on the quality of their technology and rights.

Technology markets · patent holders
Share of enterprise value in intangibles85%+
That is why RenaAi builds its own alternative data on market areas, equities and patentsSee our three datasets

KRW amounts converted at ₩1,340 = US$1 (October 2026).

Evidence

Why it can’t be copied,
even with the same public data

Patent · validated performance · academic credibility · demand pipeline.
RenaAi’s real value lies in the methodology that turns market-area data into credit risk.

Patent, 9 claimsOut-of-sample AUC 0.8325Submitted to SSCI journalDemand pipeline

AUC 0.8325 is the discriminatory power of predicting default among small businesses with no credit history using market-area data alone, scored on a period not used to train the model (out-of-sample). Closer to 1 is more accurate.

Four checks the methodology has passed

  • Patent (IP)

    Patent application filed with 9 claims through patent attorney Kyung-min Woo (우경민) of Meta Patent Law Office. It protects the thin-file scoring methodology itself (Claim 9), not just its output.

    Anyone using the same method has to design around the claims. The first to file sets the standard.

    claims9

  • Validated performance

    With zero credit history and market-area data alone, the model reaches an out-of-sample AUC of 0.83 (an accuracy measure; closer to 1 is better), above the 0.70–0.80 range of card issuers’ retail scoring built on decades of payment history.

    Accuracy rises further when personal credit data is added through pseudonymized data linkage and SCB.

    AUC0.8325

  • Academic validation

    Researchers affiliated with NUS AIDF, co-founded by MAS, NRF and NUS (Seyoung Park and Hyuntak Lee), and a paper submitted to Finance Research Letters (an SSCI-indexed journal) support the credibility of the methodology.

    Alternative credit depends on adoption by regulators and financial institutions, so peer-reviewed evidence is needed.

    SSCIFRL submitted

  • Demand & pipeline

    We have a pipeline of pilots and collaborations with financial institutions and the core lenders to small businesses, including savings banks, capital (consumer finance) companies and internet-only banks.

    Validated model + demand that already exists = lower commercialization risk.

    demandLenders
Result: out-of-sample discrimination0.8325

Three datasets

The three kinds of alternative data RenaAi builds

Each dataset goes through source collection, normalization and modeling to become an indicator financial institutions use. The methodology behind this process is undergoing public validation through a patent application and academic papers.

Alternative credit scoring for small businesses

A source data asset that uses market-area data to estimate the probability of default (PD) for businesses with no credit history.

Market-area dataIllustrative

Market area × sector × quarter cells

Low risk signalHigh
  • Makes 3.16 million thin-file small businesses assessable
  • Zero credit history, market-area data only: out-of-sample AUC 0.83
  • Delivered by API to lenders and rating agencies, expanding into market-area indices and real estate valuation

Patent application filed (9 claims) · submitted to Finance Research Letters

See the data structure

Equity alternative data

A data asset that normalizes filings, prices and trading flows to read listed companies’ growth and risk before financial statements do.

Equity dataIllustrative

Leading signal vs. filed record

Alternative data signalReflected in filings
  • Normalizes regulatory filings (DART), prices and trading flows into time series on one standard
  • Captures change as a signal before it reaches financial statements
  • Delivered in a form portfolio and research teams use for stock screening and risk monitoring

Our CEO, with a background in quant hedge funds and global equity management, judges whether signals connect to prices

See equity signals

Patent intelligence

A data asset that reads patents by quality, not count, and turns them into new signals for corporate valuation and investment.

Patent dataIllustrative

Four axes of patent quality

Forward citations
Global family
Enforceability
Design-around difficulty
  • Quantifies forward citations, global family, enforceability and design-around difficulty
  • Measures the real competitive strength of tech companies beyond financial statements
  • Base signals for IP finance: valuation, collateral and securitization

Our CLO, a patent attorney and lawyer, reviews the scope of rights; the finance side assesses value

See IP intelligence

All three datasets go through the same methodology (normalization → signal separation → standardized indicators).

Methodology

Three steps from scattered source data to indicators finance can use

Collection alone is not enough for financial institutions. RenaAi aligns data in different formats on one standard, filters out the noise, and delivers numbers that underwriting and investment teams can read right away.

  1. 1

    Multi-dimensional ontology normalization

    Integrating disparate sources

    We align unstructured data with entirely different formats and frequencies (spatial coordinates, DART filing text, patent claims) on a single standard. More than 130,000 spatial and time-series records are standardized into market area × sector × quarter cells, creating a comparable base.

  2. 2

    Separating noise from leading signals

    Financial engineering modeling

    More data does not by itself create value. Drawing on a fund manager’s valuation experience and the methods of NUS credit risk researchers, we remove short-lived trends and illusions (noise) and extract only the valid signals tied to actual default risk and enterprise value.

  3. 3

    Standardized indicators for regulated finance

    API & data feed

    We convert complex raw data into single indicators that loan officers at lenders and fund managers at asset managers can use in decisions right away. Outputs include probability of default (PD), a leading market-area risk index and a patent quality score, delivered through a REST API.

Business model

We supply data and earn from usage and subscriptions.

RenaAi does not sell scores under a credit rating license. We are a B2B data company that supplies source data financial institutions and asset managers feed into their own models. Revenue comes from API usage fees and annual data feed subscriptions. Here is how we differ from traditional credit bureaus.

Traditional credit bureaus (CB license)

A regulated utility industry

Regulation & licenseFinancial Services Commission (FSC) license required · strict capital rules and government oversight
Revenue & marginFixed per-query fees of a few US cents · no pricing power
Data scopePast loan and card payment history everyone sees (conventional financial data)
Scalability & valuationStays a domestic financial utility · low valuation multiples (P/E 5–8x)
Focused on past history · no pricing power

RenaAi

Alternative data supply — turning source data into indicators delivered via API and data feed

Regulation & license
No license required · a pure data supplier adopted directly by regulated financial institutions
Revenue & margin
B2B API usage fees and annual data feed subscriptions (recurring revenue)
Data scope
Market-area network dynamics · leading equity trading flows · patent quality scores (source alternative data)
Scalability & valuation
Patent-protected data methodology · a structure that can scale across countries and asset classes
API usage feesData feed subscriptionsNo license requiredPatent-protected methodology

Global precedent

$3.2B

All-cash acquisition · 13x revenue

Why BlackRock spent $3.2B on a data company

Preqin, the private markets data company acquired in 2024 by BlackRock, the world’s largest asset manager, is not a credit bureau. It had systematically built alternative data on private fund markets, where there are no public filings. That is why, with annual revenue of around $240M, it was acquired for more than 13 times revenue. This source alternative data model is exactly what RenaAi is building across small-business market areas, equities and patents.

Team

A global team connecting data to world financial markets

A CEO who has worked on credit risk and asset management in Seoul, Singapore, Switzerland and the US; a CLO who is both a patent attorney and a lawyer; and a COO, a crypto specialist who served global hedge funds at Société Générale in Hong Kong. Together they cover value, rights and connection.

CEO

Byung-Geun Choi

“I have never stopped at analyzing data. I have looked for the signals that move capital.”

He researched global credit risk modeling at the National University of Singapore (NUS RMI), then spent 10 years leading valuation and portfolio management covering thousands of companies at Samsung Asset Management, NH-Amundi and Korea Investment Management.

Background
  • Seoul National University, Electrical Engineering
  • EPFL, Master’s in Management of Technology
  • University of Michigan, Ann Arbor, Industrial Engineering doctoral program (coursework completed)
  • Credit risk modeling researcher, NUS Risk Management Institute (RMI)
  • Global equity fund manager, Samsung Asset Management
  • Head of global equity team, NH-Amundi Asset Management
  • Head of product strategy, Korea Investment Management

Role at RenaAi

Byung-Geun Choi draws on his experience in asset management and credit risk research to lead RenaAi’s alternative data methodology, credit and valuation models, and data supply business.

CLO · Head of IP

Yoo Yeon

“We turn technology into rights, and defend those rights in court to turn them into capital.”

An IP lawyer with seven years of patent attorney practice filing SK hynix patents. After graduating first in class from Sungkyunkwan University School of Law, Yoo Yeon handled advanced-technology IP disputes at the major law firms Shin & Kim and Barun Law.

Background
  • Seoul National University, Electrical Engineering (same 2004 entering class as the CEO)
  • Patent attorney, Shinsung Patent Law Firm (SK hynix patents)
  • Sungkyunkwan University School of Law, graduated first in class
  • IP attorney, Shin & Kim and Barun Law
  • Managing Partner, Libro Law Firm

Role

Leads patent intelligence and IP rights acquisition and enforcement

seoyeonpapa.com

Co-founder · COO

Michael T.

A former Relationship Manager at Société Générale CIB in Hong Kong who connected financial products to global hedge funds, and a crypto specialist who has managed and invested in digital assets directly.

Background
  • Université Paris 1 Panthéon-Sorbonne, Master’s in Banking & Finance (with honors)
  • Analyst, Columbia Threadneedle Investments
  • Global Markets RM · team lead, Société Générale CIB Hong Kong
  • VP, Trading & Investment, Asia Innovations Group
  • Co-founder · COO, AIE Labs

Role

Leads global business development, institutional partnerships and operations

Full background

“Finance (Byung-Geun Choi) sets the value, law (Yoo Yeon) protects the rights, and global markets (Michael) open the connection to the market.”

When financial institutions adopt data, they ask about the basis for value, the scope of rights and the path to adoption. One team answers all three together.

Data catalog

See all three datasets and the evidence behind them in one place.

All data goes through the same methodology (normalization → signal separation → standardized indicators). Click any item to see the data structure, validation method and case studies on the detail page.

0.8325

Out-of-sample AUC

Zero credit history · market-area data only

9

Patent claims

Patent application filed for the scoring methodology

3.16M

Blind spot made assessable

Thin-file small businesses

SSCI

Finance Research Letters

Co-authored with AIDF-affiliated researchers · submitted

Market-area data

Alternative credit scoring for small businesses

Equity data

Signals of change before filings

Patent data

Patent quality score

Validation · research

Model validation and research lineage

Enterprise value

What the data produces

Data PoC

Want to add alternative data to your models?

See for yourself, with your own data, how much market-area and patent signals improve your existing scoring models. A valuation check is also available.

  • PoC combining market-area and patent signals with your portfolio or scoring models
  • Validation of PD estimates for businesses with no credit history
  • Connecting patent quality indicators to corporate valuation and investment signals
  • Completely free · confidential · led directly by our CEO, a former fund manager
Data partnership & supply inquiryRequest a valuation check

We will contact you within 24 hours of your request · no sales calls