Data Quality

Data Quality Features - Give information about the quality of data for specific user, include how recent data is
They are returned in the srilanka/features/data-quality endpoint. Definitions of these features generated by Pngme are:

FeatureDefinitionUse CaseValue PropositionResponse value
data_density_{t0_days}_{t1_days}Ratio of active user days to total number of days in range, over the previous t0 to t1 days, where the time windows are 0-30, 31-90, or 0-90 days history prior to the prediction date.• Customize the user experience, displaying relevant information or features more prominently for users with higher engagement ratios
• Offer incentives, promotions, or rewards to re-engage users who have been less active, encouraging them to explore additional services
• Proactively address the needs of users whose engagement has decreased, offering personalized solutions to retain their loyalty
• Implement usage-based fee structures for certain services based on user engagement ratios
• Understand how frequently users interact with financial services and products, enabling institutions to tailor their offerings accordingly
• Categorize users into segments such as high-engagement, moderate-engagement, and low-engagement, allowing for personalized communication and product recommendations
• Optimize the timing, frequency, and channels of communication, ensuring that messages are well-received by actively engaged users
• Prioritize product development and features based on user engagement ratios
{float, null}
data_oldest_minutesThe time in minutes between utc_endtime and the oldest financial event or alert, as an indicator of data age, or earliest activity within a 180 day period.• Actively track transactions which occured farthest to utc_endtime to identify potential
anomalies or suspicious activities
• Send relevant communications to users based on their earliest
financial events such as trigger notifications, alerts, or reminders to keep
users informed about loyalty offers.
• Adjust risk scores based on the age of financial events, ensuring that
risk assessments are reflective of the earliest user behavior
• Target users financial activity legth, tailoring marketing messages
and offers to align with their current financial needs and interests
• Leverage the time difference to dynamically update reporting and dashboards
by working with the earliest financial data, providing a more accurate and timely
picture of the current financial landscape
• Set thresholds based on the age of financial events, flagging or investigating
transactions that deviate from expected recency patterns
• Send targeted communications, alerts, or offers to users, taking into account their
earliest financial activities
{int, null}
data_recency_minutesThe time in minutes between utc_endtime and the most recent financial event or alert, as an indicator of data recency, or freshness.• Actively track transactions occurring close to utc_endtime to identify potential anomalies or suspicious activities
• Send timely and relevant communications to users based on their recent financial events such as trigger notifications, alerts, or reminders to keep users informed about their financial activities
• Adjust risk scores based on the recency of financial events, ensuring that risk assessments are reflective of the latest user behavior
• Target users with recent financial activities, tailoring marketing messages and offers to align with their current financial needs and interests
• Use the time difference as an indicator of data recency to make real-time decisions
• Leverage the time difference to dynamically update reporting and dashboards by working with the latest financial data, providing a more accurate and timely picture of the current financial landscape
• Set thresholds based on the recency of financial events, flagging or investigating transactions that deviate from expected recency patterns
• Send targeted communications, alerts, or offers to users, taking into account their recent financial activities
{int, null}