How Einstein aiDeal Processes 360B+Data Points to Deliver Credit Decisions in Seconds 

Introduction 

Speed has always mattered in lending, but it has rarely been achieved without compromise. For decades, faster approvals meant looser controls, while rigorous risk analysis required time, manual review, and operational delay. That trade-off is no longer acceptable. Borrowers expect near-instant responses, regulators expect defensible decisions, and lenders need both precision and scale. 

Einstein aiDeal was built to address this tension directly. By leveraging AI, machine learning, real-time data, alternative data, and more than 360 billion data points, it delivers reliable automated credit decisions in seconds without sacrificing nuance or detailThis is not acceleration for its own sake. It is the result of a carefully engineered decision engine designed to operate at scale, in real time, under real-world constraints. 

The Challenge of Speed at Scale in Lending 

Speed becomes exponentially harder as volume, data diversity, and regulatory obligations increase. A lender assessing a few thousand applications a month may be able to afford manual checkpoints; however, once the volumes increase, it is no longer feasible. The real bottleneck is not computation alone. It is orchestration. Data arrives from multiple sources, in different formats, with varying degrees of reliability. Models must simultaneously evaluate risk, affordability, fraud, and compliance. Decisions must be consistent across channels and explainable after the fact. 

This complexity is why many institutions still struggle to answer a basic question: how are credit decisions made at scale without introducing delay or inconsistency? Einstein aiDeal addresses this by redesigning the decision flow itself, rather than simply automating individual steps. 

What Does “360B+ Data Points” Mean?  

The figure is not a marketing tool. It reflects the cumulative volume of individual data signals processed across transactions, accounts, time periods, and events. These data points are not static records; they are dynamic observations that evolve as borrower activity changes. 

Einstein aiDeal ingests granular transaction-level data from Open Banking and Open Accounting sources, enriched with alternative datasets and internal lender inputs. Each transaction may generate dozens of evaluative signals such as frequency, timing, counterparties, volatility, and trend direction. Over time, this creates a high-resolution borrower profile that far exceeds what traditional underwriting models were designed to handle. The value lies not in the size of the dataset, but in how those signals are structured, validated, and interpreted in real time. 

Pulse’s Einstein aiDeal leverages AI, machine learning, real-time data, and alternative data sources to auto-decide on 95% of incoming deals with minimal human intervention. Each deal is auto-decisioned in under 45 seconds each, with customisable criteria and thresholds. To learn more about Einstein aiDeal, contact us today. 

Einstein aiDeal’s Data Ingestion Architecture 

At the heart of Einstein aiDeal is a real-time ingestion layer built for continuous flow, not batch processing. Data enters the system through secure APIs, is standardised immediately, and is validated before being passed to decision logic. Data ingestion is context––aware. The engine does not treat all data equally. It prioritises signals based on relevance to the specific lending product, criteria/thresholds, and borrower profile being assessed. This prevents noise from overwhelming decision accuracy and allows the system to remain responsive even under heavy load. 

Latency is managed through parallel processing pipelines. Rather than waiting for all data to be fully analysed, Einstein aiDeal begins decisioning as soon as minimum viable thresholds are met. This architectural choice is key to delivering credit decisions in seconds rather than minutes. 

AI Models Powering Instant Decisions  

Speed without intelligence is automation; speed with intelligence requires layered modelling. Einstein aiDeal uses multiple AI and machine learning models, each optimised for a specific purpose: affordability assessment, exposure analysis, and potential risk. These models operate simultaneously. Outputs are synthesised through its decision engine, which weighs signals according to lender-defined risk appetite or criteria with embedded compliance. This ensures decisions are not only fast but also aligned with policy. 

Why Speed Does Not Compromise Risk 

The assumption that faster decisions are weaker decisions is rooted in legacy processes, not modern architecture. Einstein aiDeal achieves speed by removing friction, not scrutiny. Risk is assessed continuously rather than episodically. Instead of relying on static snapshots, the system evaluates financial data and signals in real-time. This depth allows it to identify early warning signals that slower, document-driven processes often miss. 

Explainability is built in. Each decision can be traced back to contributing factors, model outputs, and policy thresholds. This transparency supports regulatory review and internal governance, reinforcing confidence in fast credit decisions. The result is a solution that is not only fast but also resilient. Decisions remain consistent under pressure, even as volumes spike or market conditions deteriorate. 

Conclusion

Einstein aiDeal demonstrates that scale, speed, and rigour are no longer mutually exclusive. By processing over 360 billion data points through a purpose-built decision architecture, it delivers credit outcomes in seconds without sacrificing depth or control. 

For lenders still restricted by manual bottlenecks or fragmented systems, the question is no longer whether faster decisions are possible. The question is whether existing processes can keep pace with borrower expectations and competitive realities. How are credit decisions made in this new paradigm? Understanding this requires looking beyond individual models to the entire decision ecosystem. Einstein aiDeal represents a shift toward that integrated future—where intelligence, speed, and confidence operate as one.