Complaints 2

Creating a comprehensive fraud complaints dashboard is an excellent step to track, analyze, and address fraud issues effectively. Here’s a detailed guide focused on fraud types and how to deal with them, followed by how to structure your dashboard:

1. Common Types of Fraud in Banking

Fraud complaints can typically be categorized into the following types:
1. Unauthorized Transactions
• Customer reports a transaction they didn’t initiate.
• Examples: Stolen card, hacked account, skimming.
• Key Metrics:
• Number of complaints.
• Financial impact (total amount disputed).
• Resolution time.
2. Phishing/Smishing
• Fraudsters impersonate the bank to steal personal information via email, text, or calls.
• Key Metrics:
• Volume of phishing-related complaints.
• Channels affected (email, phone, etc.).
• Resolution rates.
3. Identity Theft
• Fraudsters open accounts or apply for credit using stolen identities.
• Key Metrics:
• Volume of identity theft complaints.
• Associated financial losses.
• Geographic hotspots.
4. Card Not Present (CNP) Fraud
• Fraudulent online transactions where the card isn’t physically present.
• Key Metrics:
• High-risk merchants and channels.
• Transaction trends (time of day, value).
5. Chargeback Fraud (Friendly Fraud)
• Legitimate customers dispute valid charges to reverse payments.
• Key Metrics:
• Frequency of chargebacks.
• Disputed vs. resolved cases.
6. Social Engineering Fraud
• Victims are tricked into transferring money directly to fraudsters.
• Key Metrics:
• Types of scams (lottery, tech support).
• Amounts involved.

2. Dashboard Design Principles

Focus on creating a dashboard that is actionable, easy to interpret, and highlights key fraud trends. Use the following structure:

Key Components:
1. Overview Section:
• Total fraud complaints (current month vs. previous month).
• Breakdown by fraud type (e.g., pie chart or bar chart).
• Average resolution time.
2. Trends Analysis:
• Monthly trends for fraud complaints over time.
• Resolution rates over time.
3. Impact Metrics:
• Financial loss associated with fraud complaints.
• Recovery rates (money recovered vs. lost).
4. Geographic Insights:
• Fraud hotspots based on customer locations.
• Merchant locations involved in fraud complaints.
5. Resolution Performance:
• SLA adherence (percentage of complaints resolved within SLA).
• Unresolved vs. resolved complaints.
6. Customer Sentiment (Optional):
• Sentiment analysis of complaint descriptions.
• Trends in negative vs. positive feedback.

3. Building the Dashboard: Data Preparation

a. Aggregated Metrics:
1. Fraud Volume by Type:
• Count of complaints grouped by Fraud_Type.

proc sql;
select Fraud_Type, count(*) as Complaints_Count
from fraud_complaints
group by Fraud_Type;
quit;

2.	Resolution Times:
•	Average resolution time by fraud type.

proc sql;
select Fraud_Type, mean(Resolution_Time) as Avg_Resolution_Time
from fraud_complaints
group by Fraud_Type;
quit;

3.	Financial Impact:
•	Total disputed amount by fraud type.

proc sql;
select Fraud_Type, sum(Transaction_Amount) as Total_Loss
from fraud_complaints
group by Fraud_Type;
quit;

b. Trends Over Time:
1. Monthly Complaints Trends:

proc timeseries data=fraud_complaints out=monthly_trends;
id Complaint_Date interval=month;
var Complaint_Count;
run;

c. Sentiment Analysis (Optional):

If complaint descriptions are available, perform basic sentiment analysis.

proc sentiment data=fraud_complaints out=sentiment_results;
var Complaint_Description;
run;

proc freq data=sentiment_results;
tables Sentiment_Category;
run;

4. Suggested Visualizations
   1. Fraud Breakdown (Bar Chart):
      Show the distribution of complaints by fraud type.

proc sgplot data=fraud_complaints;
vbar Fraud_Type / response=Complaints_Count;
title "Fraud Complaints by Type";
run;

2.	Complaint Trends (Line Chart):

Visualize how fraud complaints vary over time.

proc sgplot data=monthly_trends;
series x=Month y=Complaint_Count;
title "Fraud Complaint Trends Over Time";
run;

3.	Geographic Hotspots (Map):

Use a geographic map to highlight fraud hotspots.

proc sgmap plotdata=fraud_complaints;
bubble x=Longitude y=Latitude size=Complaints_Count / transparency=0.5;
title "Fraud Complaint Hotspots";
run;

4.	Financial Impact (Heatmap):

Show financial loss by fraud type and region.

proc sgplot data=fraud_complaints;
heatmap x=Region y=Fraud_Type / colorresponse=Total_Loss;
title "Financial Impact of Fraud by Region and Type";
run;

5. Tools for Interaction

If using a tool like SAS Visual Analytics, you can create dynamic dashboards:
• Add filters for fraud type, regions, or dates.
• Enable drill-down capabilities (e.g., click on a fraud type to view complaint details).

6. Reporting and Insights
   1. Automate Dashboard Updates:
      Schedule your SAS program to refresh data daily or weekly using macros.

%macro refresh_dashboard;
/* Add data processing and visualization code here */
%mend;
%refresh_dashboard;

2.	Highlight Key Takeaways:

Add insights to accompany visuals:
• Which fraud type has the highest volume?
• Where are fraud hotspots emerging?
• Are resolution times improving or worsening?

Would you like help with SAS code for specific visualizations or guidance on using SAS Visual Analytics for an interactive dashboard?