Data quality for accurate AI fraud and risk scores

Why data quality matters

The efficacy of AI predictions hinges on the quality of the underlying data used for training and updates, as highlighted in a recent whitepaper by American Banker.

In the realm of fraud prevention and risk management, data quality is paramount, impacting accuracy, timeliness, strategic decision-making, and profitability. Instances of incomplete data can have negative impacts on results. According to a 2021 report by Gartner, poor data quality costs organizations an average of $12.9 million annually, leading to revenue loss and hindering decision-making processes.

Data quality is the basis of accurate risk scores

Fraud and risk management are dynamic fields, with sophisticated fraudsters continually adapting their tactics. Data quality affects response times, identification of evolving threats and detection of fraud patterns.

The benefits of quality data extend beyond identifying fraud and other financial crime; the add-on benefits are more complete information to enhance business performance by identifying operational efficiencies and protecting brand reputation (e.g. fewer false positives and less friction) while increasing profitability.

7 attributes of quality data

Data quality begins with accuracy, ensuring that records are error-free, facilitating prompt and correct transactions, and fostering trust in the system. The following attributes characterize quality data:

• Consistency: Ensuring no conflicts in different datasets or databases.
• Accuracy: Data is exact and error-free with no typos, redundancies or outdated entries.
• Completeness: Data records are full and contain no missing or incomplete elements.
• Auditability: Data is accessible and includes a history of changes.
• Validity: Data adheres to the organization’s standard formats and values.
• Uniqueness: There are no duplicate records.
• Timeliness: Data is updated and available when needed.

Data quality and regulatory compliance

Effective data governance is pivotal for risk management and compliance. A robust Data Governance Framework establishes policies, procedures and responsibilities for data management, including data cleaning and preparation.

International payments organizations must be compliant with data privacy practices globally, regionally and locally. For example, the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA) necessitate not only high-quality data but also ethical use within established guidelines. Data governance frameworks ensure that organizations handle sensitive information ethically and responsibly, safeguarding against regulatory breaches and potential financial losses.

Using a company’s own historical data for AI models

To combat financial crime effectively, organizations rely on risk scores derived from extensive and accurate data. Obtaining, preparing and updating the data for AI models requires significant expertise and resources. For many businesses, this presents a significant barrier.

The major barriers to implementing AI to address fraud and money laundering is a shortage of data science talent (39%), according to the respondents of a joint survey by American Banker and Brighterion, followed by time to implement (35%) and model governance (34%). Despite these challenges, 62 percent of respondents stated that AI is critically or highly valuable to fighting fraud.

Cleaning and preparing historical data involve a systematic approach.

• Data profiling
• Data quality assessment
• Data standardization
• Data privacy and security
• Data documentation
• Data traceability and lineage
• Data validation and reconciliation
• Auditing and monitoring
• Training and awareness
• Continuous improvement

Fast AI implementation with globally sourced, quality data intelligence

There are market-ready solutions available today, offering streamlined approaches to fraud prevention, but finding one trained on high-quality, globally sourced data is paramount.

Global data will have learned the patterns of the most difficult fraud and those of authentic transactions worldwide. This knowledge is key for the model to recognize and provide highly accurate scores. The larger the source of intelligence the better.

Superior models are built and trained with quality intelligence derived from anonymized, aggregated transaction data, while ensuring compliance with local and global regulations. The ability to additionally integrate an organization’s proprietary data enhances model accuracy, while real-time updates ensure responsiveness to evolving threats.

“The key is to find market-ready AI models built with global transaction intelligence, enabling them to work effectively right out of the box,” according to the American Banker whitepaper.

Market-ready models also speed up implementation times. Ideally, the model will be ready to deploy in just a few days, enabling organizations to bypass the need for lengthy design, building, integration and deployment schedules.

Experience is key

In the rapidly evolving landscape of fraud prevention and risk management, data quality emerges as a linchpin for success. By prioritizing data quality and leveraging market-ready AI solutions, organizations can fortify their defenses against financial crime while driving business performance and profitability. With the right approach and highly experienced partners, organizations can navigate regulatory complexities and harness the power of global data intelligence to stay ahead in an increasingly digital world.

Learn more about data quality and market-ready AI in Market-ready AI offers a faster path to fraud mitigation by American Banker.