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AI to Revolutionize

ByInfima

How Infima Uses AI to Revolutionize Mortgage-Backed Securities

Infima Technologies, a pioneer in financial technology, is pushing the boundaries of predictive analytics for the mortgage-backed securities (MBS) market. Founded by Kay Giesecke, a Stanford professor and deep learning expert, Infima has developed groundbreaking decision-support tools for portfolio managers and traders using advanced AI models. This new approach, created to address the specific complexities of debt markets, offers insights into MBS performance with a clarity that was previously out of reach.

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Origins and Vision of Infima’s Predictive Platform

The story of Infima begins in academia, where Giesecke noticed a gap in AI applications within the financial sector. While autonomous vehicles and voice recognition thrived with AI, financial markets lagged, particularly in mortgage analytics. Recognizing an untapped opportunity, Giesecke set out to build a large-scale deep learning system focused on forecasting borrower and company behaviors. The mission: use AI to unlock insights in MBS that could guide investors and reduce market inefficiencies.

The Importance of High-Quality Data Sources

Data is at the heart of Infima’s approach. The company’s models are built on vast, historical datasets, including decades of mortgage records. By analyzing how borrowers have behaved across different economic environments, Infima’s platform generates nuanced predictions. According to Giesecke, data from government agencies and data aggregators play a crucial role, providing transparency into the market. This allows Infima to track behavior patterns that influence MBS values and ultimately guide better investment decisions.

Data SourcePurpose
Government agenciesProvide public mortgage data for transparency
Data aggregatorsOffer extensive datasets for licensing
Historical mortgage recordsTrack borrower trends across decades

Decoding Borrower Behavior with AI

Understanding borrower behavior is essential for predicting MBS performance. Infima’s models consider a wide range of behavioral patterns. For instance, in predicting loan repayment, the AI analyzes borrower actions under varied economic conditions. Giesecke emphasizes that these patterns are complex; individual circumstances, combined with broader macroeconomic factors, can drastically impact borrower actions. Infima’s deep learning algorithms classify behaviors, enabling financial institutions to anticipate potential risks or rewards tied to specific securities.

One of the challenges in AI-driven finance is transparency. Clients are increasingly asking for “explainable AI”—a system that not only predicts but also clarifies the reasoning behind its forecasts. Giesecke has prioritized this aspect at Infima, investing in tools to make complex AI models understandable to users. Clients, he explains, don’t just want accurate predictions; they want to understand the “why” behind them. This emphasis on clarity not only builds trust but also empowers investors to make informed decisions.

Application of Infima’s AI Models Across Financial Use Cases

Infima’s AI technology, though focused on the mortgage sector, has potential applications across multiple financial use cases. By clustering and categorizing borrower behaviors, Infima’s models are able to project borrower and company actions with high accuracy. This predictive power is invaluable to portfolio managers, traders, and financial institutions who need to make real-time decisions. The platform’s core use cases currently include mutual funds, insurance portfolios, and trading desks, with plans to expand into adjacent sectors.

Giesecke notes that mutual fund managers, for example, often rely on a team of analysts to analyze data and determine the risk profile of securities. Infima’s AI model enhances this process by projecting the performance of individual securities, enabling fund managers to select the most promising assets while avoiding high-risk investments. As Giesecke explains, “It’s about providing portfolio managers with actionable data on which securities have the potential to perform and which do not.”

Expansion Opportunities Beyond the Mortgage Sector

While Infima’s current focus remains on the mortgage market, its model holds promise for broader financial markets. Giesecke envisions expanding into sectors like auto loans, student loans, and municipal bonds, which face similar challenges in credit risk and payment behavior forecasting. The technology can also adapt to consumer loans bundled into asset-backed securities and collateralized debt obligations in the corporate sector, making it a versatile tool for credit analysis across various industries.

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Exploring the Impact of Climate on Financial Markets

Infima is also pioneering research into climate-related risks within the mortgage-backed securities market. Events like wildfires, hurricanes, and floods pose a growing threat to property-backed assets, and investors are increasingly concerned about exposure to these climate risks. Infima’s initiative aims to provide tools for understanding and mitigating the financial impact of environmental factors on MBS. Giesecke explains that by mapping climate risk, Infima’s clients can make more informed decisions about securities with high exposure to climate-sensitive areas, such as California and the Southeastern United States.

Infima’s Go-to-Market Strategy and Pricing Model

As Infima grows, its go-to-market strategy includes offering its platform to a diverse range of financial clients, from large mutual funds to smaller, specialized firms. The company uses a subscription-based SaaS model, with pricing that scales based on factors such as assets under management, specific use cases (research, trading, reporting), and the number of data analysts using the platform. Subscription fees range from $50,000 to $700,000, reflecting the depth of analytics provided and the customizability of the solution to suit varied client needs.

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