MoneyLion delivers AI- powered personal finance products with Chalk

MoneyLion is on a mission to empower Americans to make better financial decisions. With millions of users across lending, investing, and personal finance tools, the company relies on a complex machine learning ecosystem to drive real-time fraud detection, customer engagement, and personalized recommendations.

As the business scaled, building and deploying machine learning solutions across teams—ML operations, backend engineering, product, and data science—became more challenging. Each group owned a different part of the ML lifecycle, with distinct priorities:

• Feature Platform Team (FPT) backend engineers optimized ingestion pipelines, minimized query latency, and ensured data quality.
• MLOps engineers focused on scalability, governance, and platform reliability.
• Data scientists needed to iterate quickly with Python, without infrastructure barriers.
• Product managers pushed for reusable, production-grade features that could drive business results.

The result was natural friction. Different workflows, goals, and metrics made collaboration slow and costly. MoneyLion needed more than a better feature platform. They needed an alignment layer for the entire ML lifecycle—from idea to production.

That alignment didn’t mean forcing teams into the same process. It meant creating a shared space where data scientists could build, engineers could scale, MLOps could govern, and product teams could move quickly. Without that layer, features were delayed, duplicated, or lost between teams.

Designing a feature platform that fits all team use cases—and still performs—was one of our biggest challenges.

Meng Xin Loh Technical Product Manager

MoneyLion’s first-generation feature platform was technically robust but operationally fragmented. Built around Java-based micro-services using SpringBoot, Postgres, and Redis, the system prioritized scalability — but also introduced complexity.

Data scientists and ML scientists, like Jing, prototyped features offline in SQL or notebooks. Bringing these experiments into production required rewriting logic in Java, often by different engineers on the Feature Platform Team (FPT). This translation step added significant latency between experimentation and deployment.

We would prototype offline, then wait for engineers to rewrite everything for production. It was slow and disjointed.

Jing Chong Beh Senior Machine Learning Scientist

The backend engineers, like Anya, maintained multiple custom micro-services for different products, with duplicated ingestion and feature computation logic. Without a centralized feature catalog or lineage tracking, feature reuse was rare and offline/online skew was common—especially for real-time applications.

Despite heavy investment in Postgres query optimization and Redis caching layers to improve read performance for fraud use cases, maintaining sub-second latencies remained difficult under peak loads. Many fraud models required features to be computed and served within hundreds of milliseconds to meet strict SLA targets.

There were no reusable features or central catalog. Every use case started from scratch, and supporting that at scale was overwhelming.

Anya Gurova Senior Backend Engineer

MLOps engineers, like Melvin, managed governance manually — controlling data access, code promotion, and observability across a patchwork of microservices — without a unified interface for lifecycle management.

Without a cohesive system, features were delayed, duplicated, or dropped—limiting MoneyLion’s ability to deliver real-time, AI-driven experiences at scale.

Chalk unified MoneyLion’s fragmented ML workflows by providing a developer-first platform that lets each team contribute effectively without forcing a rigid process.

How MoneyLion transformed their ML workflow

For backend engineers, Chalk replaced manual micro-service maintenance with dynamic feature pipelines. Engineers now focus on scaling system throughput, onboarding new real-time data sources, and optimizing query planners, rather than hand-translating feature logic.

After switching to Chalk’s Query Planner, latency stabilized across peak load days. Even end-of-month Fridays, we stayed within SLA.

Anya Gurova Senior Backend Engineer

For MLOps engineers, Chalk introduced a clean, branch-based development lifecycle. Each feature change exists in an isolated environment until promoted, reducing the risk of dependency conflicts or production regressions. Governance is enforced automatically through versioning, feature ownership tracking, and runtime policy checks.

It’s easy to get started on Chalk, and the isolation model keeps everything safe by default. Teams don’t block each other anymore.

Melvin Low MLOps Engineer

For data scientists, Chalk eliminated the offline/online skew. Features are defined once in Python, immediately versioned, and served online through Chalk’s real-time query engine. This closed the loop between experimentation and production, dramatically speeding up iteration cycles.

Chalk lets DS build in Python and Pandas—which is familiar—and not worry about infrastructure. We can experiment and iterate much faster.

Jing Chong Beh Senior Machine Learning Scientist

For product managers like Meng, Chalk unlocked better observability and feature reuse across lines of business, reducing duplicate effort and shortening time-to-market.

Now teams contribute to features instead of requesting them. That changes everything.

Meng Xin Loh Technical Product Manager

Chalk acts as the central feature platform between MoneyLion’s data infrastructure and real-time model serving systems. It abstracts feature computation, storage, and online serving behind a unified Python-first interface.

Teams define, test, version, and serve features through Chalk while maintaining full traceability and runtime guarantees.

Chalk helped MoneyLion not only unify the process of productionizing their ML but also accelerate customer-facing innovation across key areas.

Today, with Chalk powering real-time ML infrastructure, MoneyLion can:

• Protect users with real-time fraud detection: Features served within tight latency budgets enable faster risk decisions at the time of transaction.
• Deliver smarter budgeting and spend insights: Personalized finance nudges are surfaced based on live transactional behavior.
• Surface contextual feed and lifecycle recommendations: Adaptive recommendations dynamically adjust to user context in real-time.

These capabilities are active today, reaching millions of MoneyLion users.

Chalk helps us deliver financial products that are more responsive, more personalized, and more secure for millions of users. It’s a direct line from infrastructure to impact.

Meng Xin Loh Technical Product Manager

Internal alignment drives external product impact

Chalk now serves as the foundation for MoneyLion’s future AI strategy. As the company grows, Chalk is enabling:

• Broader feature ownership across product lines.
• Faster development cycles from experimentation to production.
• Real-time architecture that scales with user growth and product and data complexity.

We’re using Chalk to embed AI everywhere—from smart budgeting to fraud to lifecycle engagement. It’s foundational now.

Meng Xin Loh Technical Product Manager