Summary

BlueFire AI is a capital market technology company, leveraging big data and high performance computing in pursuit of risk based data insights.

Our customer base spans the Institutional investment industry including Investment Banks, Soveregin Wealth, Hedge Fund, Asset Management, Private Banks and Wealth Managers.

Our core intellectual property lies in combinatoric decision models extracting actionable insight from structured and unstructured data.

The end expression of our technology is the creation of the Investment Industry's first AI Risk Analyst - Emmalyn AI. (CV available upon request)

Description of fintech solution

There are three signals under the fundamental category. AScore is a seven factor model identifying situations when cashflows may structurally change in future financial statements. EScore, by contrast, is a three factor model assuming management actively managing earnings through non-cash items in the same financial statements. Lastly, Key Insights provide a high-freqency snapshot with AI curated news-based storyline of events that may structurally impact the future of the company.

For behavioral category, our Sell-Side signal ranks quantitatively all Sell-Side analysts on each company to identify leaders and identify situations when leading analysts diverge from consensus. Concurently, Buy-Side signals helps establish the resilience of the capital invested in the firm through tracking decision-making of highly convicted investors, as well as high performant investors. This analysis, of Holdings data, is performed across Equity and large Credit Holders.

Market signals include Short-Positioning which identifies short-clustering based on unique aggregated positioning on 27k stocks globally from US$ 2trn of assets at a t 1 basis and historical profiling against the free-float. TrendShift provides 7D forecasts based on algorithmic quantitative and technical momentum processes that already have long-short capital being allocated to them as stand-alone signals. Finally, Price Drop Risk is machine learning based models to identify moments of severe short-term stress in an asset.

Combined, these ten signals offer a comprehensive risk assessment of a firm, which is both robust in its performance and efficient in implementation.

Describe your key innovations

Credit Stress Measure

BlueFire AI’s [BFAI] Credit Stress Measure [CSM] provides unrestricted, un-biased and uninterrupted assessment of credit risk. It is unrestricted by any commercial agreement if the underlying data is available. It is unbiased as it systemizes a human analyst’s decision-making process inheriting the exact preferences without bias.

A major frontier in quantitative finance, is to build early warning tools to identify credit risk. While its improbable to unambiguously differentiate between a firm that will default to ones that will not, our best approach is to give a probabilistic measure of likelihood of default or impending credit event. We present a solution which aggregates market and accounting-based measures into a structural model to obtain a firm’s value of assets and their volatility and calculate a forward-looking Credit Stress Measure.

Solution Lifecycle

Commercialized

Founder's Background

Samir Rath: An integrated circuit designer by training, Samir began his career as a macro-economist with the MAS in Singapore. He moved on to establish the algorithmic trading business in APAC for GETCO, a firm that disrupted the global exchange market making business. Samir has an intimate knowledge of data companies and understands the evolution and success factors in the growing new AI economy. Samir is a founding partner of Blue Fire AI.

Luke Waddington: A seasoned senior executive of global investment banks with over 20 years’ experience in running global businesses spanning trading, sales, prime services and electronic business lines. Luke has an unparalleled experience in taking developing businesses and positioning them through transformation to capture new sources of revenue. Luke is a founding partner of Blue Fire AI.

Founded (Years of Incorporation)

2016

How are you funded today?

Series A

Money Raised

N/A

Number of employees

20

Presence In Region

Global

Technology

Application Programming Interface (API)
Artificial Intelligence
Cloud solutions
Data automation
ESG
Machine Learning
Natural Language Processing (NLP)
Risk Assessment

HQ Country

Singapore

What are the top 3 key benefits your solution will bring to the Asset Management Industry?

Provides early warning signals from 3-6 months in advance of corporate distress, enabling effective risk mitigation ahead of asset price impairment. Competitor products provide coincident warning by which time it's often too late to avoid pnl markdown.

Enables high precision scale across a vast universe of assets at a cheaper unit cost compared to incumbent models invovling teams of in-house analysts who are bandwidth constrained.

Delivery of Blue Fire AI Risk Signals is well integrated in the existing work flow of numerous asset managers and Hedge Funds propelling adoption.

Tell us more about your past client engagements to date and your market penetration?

We are a B2B business with restricitve NDA constraints, our clients span the capital market industry. We have publicised engagements with UBS, JP Morgan, Morgans Financial, First Sentier Investors.

Do you have any case studies of your solution? If yes, please share it in less than 200 words

Emmalyn's Research Note on Silicon Valley Bank Jan'2023

Following multiple early warnings since January 2022, the Net Interest Income outlook has been under pressure as the cost of deposits and borrowing expenses continued to accumulate. As a major lender to start-ups, SVB was adversely impacted by the reduction in venture capital investment activity, coupled with higher interest rates and high cash burn among its clients. Thus, the biggest red flag was the sudden drop in funding stability (Deposit-to-Total Funding ratio), as these factors highlighted SVB's inability to retain deposits and led to a significant increase in wholesale funding in the second half of 2022. The company's fundamentals have deteriorated since the start of 2022. This trend is reflected in the spike of CSM for that year, extraordinarily high leverage and the collapse in the value of assets (-$29.5 Bio in FY22). BF Implied Rating BBB-, Leading analysts forecast downside risk, with a potential drop of 10% in EPS by 2024. Suggest Risk Reduce.

Emmalyn AI