Finance & Banking AI

Finance & Banking AI services provide structured datasets for fraud detection, risk assessment, automated trading, and financial forecasting. By ensuring high-quality data curation, these services help AI-driven financial systems improve accuracy, security, and regulatory compliance.

Algorithmic Trading Data Labeling

Involves annotating market data (e.g., price trends, volume spikes) to train AI in predicting trades. Workers tag patterns or signals, enabling automated trading strategies.

Customer Support AI Training

Focuses on creating datasets of customer inquiries (e.g., “check balance”) and responses to train AI chatbots or virtual assistants. Workers label intents or outcomes, enhancing banking support.

Financial Document Annotation

Workers label documents (e.g., loan agreements, statements) to identify key terms (e.g., “interest rate,” “due date”). This trains AI to extract and process financial information efficiently.

Fraud Detection & Anomaly Tagging

Entails tagging transactions or behaviors (e.g., unusual withdrawals) as “normal” or “suspicious.” Workers annotate data, training AI to spot fraud or security risks.

Multilingual Financial Data Processing

Involves annotating or translating financial texts across languages (e.g., contracts, reports). Workers tag or align data, supporting global banking operations.

Risk Assessment & Credit Scoring

Workers label customer data (e.g., income, payment history) to train AI in evaluating creditworthiness or risk levels. Tags like “high risk” or “approved” refine predictive models.

Where Open Active Comes In - Experienced Project Management

Project managers (PMs) are pivotal in orchestrating the development and enhancement of Finance & Banking AI systems.

We handle strategic oversight, team coordination, and quality assurance, with a strong focus on training and onboarding workers to curate the data that powers these high-stakes systems.

Training and Onboarding

PMs design and implement training programs to ensure workers understand financial concepts, regulatory standards (e.g., AML, KYC), and annotation goals. For example, in fraud detection, PMs might train workers to identify subtle anomalies, using sample transactions and guides. Onboarding includes hands-on tasks like tagging documents, feedback sessions, and calibration exercises to align worker outputs with AI needs. PMs also establish workflows, such as escalated reviews for sensitive risk assessments.

Task Management and Quality Control

Beyond onboarding, PMs define task scopes (e.g., labeling 15,000 transactions) and set metrics like precision, consistency, or fraud detection rates. They monitor progress via dashboards, address inefficiencies, and refine guidelines based on worker feedback or evolving financial regulations.

Collaboration with AI Teams

PMs connect data curators with machine learning engineers, translating technical requirements (e.g., anomaly detection thresholds) into actionable tasks. They also manage timelines to ensure data delivery aligns with AI deployment cycles.

We Manage the Tasks Performed by Workers

The annotators, taggers, or analysts perform the detailed work of preparing high-quality datasets for finance and banking applications. Their efforts are meticulous and regulation-aware, requiring precision and domain knowledge.

Common tasks include:

Labeling and Tagging

For algorithmic trading, we might tag a price spike as “buy signal.” In document annotation, they label “APR” or “penalty clause.”

Contextual Analysis

For fraud detection, our team assesses transactions, tagging “large transfer” as suspicious. In credit scoring, they analyze profiles, tagging “late payment” or “stable income.”

Flagging Violations

In financial documents, our employees and subcontractors flag unclear terms (e.g., missing dates), ensuring accuracy. In customer support, they mark unresolved queries.

Edge Case Resolution

We handle complex cases—like borderline fraud or multilingual discrepancies—often requiring discussion or escalation to financial experts.

We can quickly adapt to and operate within our clients’ annotation platforms, such as proprietary banking tools or industry-standard systems, efficiently processing batches of data ranging from dozens to thousands of items per shift, depending on task complexity.

Data Volumes Needed to Improve AI

The volume of curated data required to train and refine Finance & Banking AI systems is substantial, driven by the complexity of financial data and regulatory demands. While specifics vary by task and model, general benchmarks include:

Baseline Training

A functional model might require 5,000–20,000 labeled samples per category (e.g., 20,000 tagged transactions). For tasks like multilingual processing, this could rise to 50,000 to cover languages.

Iterative Refinement

To improve accuracy (e.g., from 85% to 95%), an additional 3,000–10,000 samples per issue (e.g., missed fraud cases) are often needed. For example, refining credit scoring might demand 5,000 new profiles.

Scale for Robustness

Large-scale systems (e.g., global banking platforms) require datasets in the hundreds of thousands to handle edge cases, markets, or rare anomalies. A fraud detection model might start with 100,000 transactions, expanding by 25,000 annually.

Active Learning

Advanced systems use active learning, where AI flags uncertain data for review. This reduces volume but requires ongoing curation—perhaps 500–2,000 samples weekly—to maintain performance.

The scale demands distributed teams, often hundreds or thousands of workers globally, coordinated by PMs to ensure consistency and compliance.

Multilingual & Multicultural Finance & Banking AI

We can assist you with your finance and banking AI needs across diverse linguistic and cultural contexts.

Our team is equipped to curate and process data for global financial applications, ensuring accurate and culturally relevant datasets tailored to your objectives.

We work in the following languages: