Working with financial data in Excel presents challenges such as reconciling transactions, analyzing loan portfolios, and verifying bank statements. The process involves connecting different datasets (e.g., loans with cash flows or borrowers), calculating key metrics (e.g., total outstanding balance), and visualizing data. Scaling this process to handle millions of transactions from multiple sources, each with its format and rules, significantly increases the complexity of ensuring accuracy and consistency.

For investors and asset managers, making decisions hinges on having access to accurate, standardized, and consistent data. Standardized data enables investors to evaluate the risks associated with various structured finance investments, guiding their investment choices. Similarly, asset managers can improve their portfolio management by having a clear and complete picture of their investments. This helps them identify potential problems and opportunities.

Our data onboarding approach at Cardo AI follows a three-step process:

1. Data ingestion:

• Data retrieval: Retrieving data from various sources (including FE applications, SFTP folders, S3 buckets, API connections) and in multiple formats (parquet, xlsx, csv, txt, etc.), such as loan databases and financial reporting systems.
• Data loading: Loading the extracted data into a centralized data lakehouse for storage and processing.

2. Data quality check:

• Data cleansing: Identifying and correcting data errors, inconsistencies, and missing values.
• Data standardization: Ensuring that data is formatted and sanitized consistently across different sources with the support of a Data Quality AI agent.

3. Data transformation:

• Data modeling: Creating a data model that defines the relationships between different data elements.
• Data transformation: Converting raw data into a format that is suitable for analysis and reporting.

Integrating an advanced data profiling tool with a data quality AI agent throughout the onboarding process offers a comprehensive understanding of dataset structures without the need for manual inspection of large source files. This combination not only automates the analysis of data structures but also provides actionable insights into data quality checks necessary to ensure consistency and accuracy.​

• Stay ahead with real-time data insights: We provide continuous input data profiling, offering insights into table and column statistics. This empowers you to make informed decisions for data quality checks and transformation logic, allowing you to verify your assumptions about the data at any given moment. By having access to up-to-date information, the AI agent identifies potential issues that you can quickly address before they impact downstream processes.
• Uncover data patterns with statistical analysis: Whether dealing with amounts, quantities, or date ranges, we offer comprehensive statistics on data distribution. This includes:
  • Numerical analysis: using average, median, mode, standard deviation, and variance to understand data dispersion and potential anomalies. Using quartiles and percentiles to understand data distribution across different ranges.
  • Outlier detection: identifies extreme values that may indicate data entry errors or inconsistencies. Use statistical thresholds to detect values that deviate significantly from historical patterns.
  • Pattern & format analysis: validates expected formats (e.g., IBAN, ISIN, phone numbers, and email structures). Ensure consistent text-based inputs across multiple sources.
  • Data drift monitoring: detect shifts in data distribution over time, highlighting potential systemic changes or unexpected anomalies.

• Identify data anomalies with ease: We help you pinpoint anomalies that might hinder data conversion. For example, if a column intended for numbers or dates contains unexpected values, the tool will highlight potential issues based on statistics like distinct values, character counts, and outliers. This eliminates the need for manual inspection of source files and provides a clear overview of data quality problems.