Complaints 1

Starting with fraud complaint analytics can feel overwhelming, but focusing on the initial steps will help you build a strong foundation. Below is a simplified, step-by-step approach to get started:

1. Understand the Complaint Lifecycle

Fraud complaints typically go through these stages:
1. Complaint Lodging:
• Customers report issues via channels like calls, emails, app forms, or branches.
2. Complaint Recording:
• Details like complaint type, description, transaction ID, and timestamps are logged into a system.
3. Investigation:
• The fraud team reviews the complaint, validates the fraud, and determines liability.
4. Resolution:
• Actions like refunds, chargebacks, or educating the customer take place.
5. Closure:
• Complaint status is updated as resolved or escalated.

Start by familiarizing yourself with your bank’s process and understanding how data flows through these stages.

2. Gather and Explore the Data

You need access to fraud complaint-related datasets. Focus on the following key data fields:

a. Structured Data (Tabular):
• Complaint Details: Complaint ID, customer ID, date, fraud type, resolution status.
• Transaction Details: Transaction ID, amount, date, merchant, and channel.
• Customer Details: Account type, location, and previous complaint history.

b. Unstructured Data (Free-text):
• Complaint Description: Customer-provided text describing the issue (e.g., “My card was used for an unauthorized transaction”).
• Email/Chat Logs: Conversations between customers and support staff.

Steps in SAS:
• Load and Review Data:

proc contents data=fraud_complaints;
run;

proc print data=fraud_complaints(obs=10); /* View first 10 records */
run;

•	Understand Missing Data:

proc freq data=fraud_complaints;
tables Fraud_Type Complaint_Description Resolution_Status / missing;
run;

3. Identify Key Questions to Answer

To guide your analytics, define the questions you aim to answer:
1. Volume Trends:
• How many complaints are logged daily, weekly, or monthly?
• Which fraud types are most common (e.g., phishing, unauthorized charges)?
2. Resolution Analysis:
• How long does it take to resolve complaints?
• Are there SLA breaches?
3. Impact of Complaints:
• What is the financial loss associated with these complaints?
• Are certain merchants, locations, or channels repeatedly involved?
4. Root Cause Identification:
• What patterns emerge from complaint descriptions (e.g., device misuse, social engineering)?

4. Start Simple Data Cleaning

Before analysis, clean your data for accuracy and consistency.

Steps in SAS:
1. Remove Duplicates:
Ensure no complaint IDs are repeated.

proc sort data=fraud_complaints nodupkey;
by Complaint_ID;
run;

2.	Handle Missing Values:

Replace missing values or flag incomplete records.

data complaints_clean;
set fraud_complaints;
if Complaint_Description = '' then Missing_Description = 1;
else Missing_Description = 0;
run;

proc freq data=complaints_clean;
tables Missing_Description;
run;

3.	Standardize Dates:

Convert dates into a consistent format for trend analysis.

data complaints_clean;
set complaints_clean;
Complaint_Date = input(Complaint_Date, yymmdd10.); /* Standardize format */
run;

5. Create an Initial Summary Report

Use basic aggregation to answer your key questions.

a. Complaint Volume by Type:

proc sql;
select Fraud_Type, count(*) as Complaint_Count
from complaints_clean
group by Fraud_Type;
quit;

b. Trends Over Time:

proc timeseries data=complaints_clean out=complaint_trends;
id Complaint_Date interval=month;
var Complaint_Count;
run;

proc sgplot data=complaint_trends;
series x=Complaint_Date y=Complaint_Count;
title "Monthly Complaint Volume";
run;

c. Resolution Time:

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

6. Categorize Complaints

Use keywords to categorize free-text complaint descriptions into fraud types or issues.

Steps:
1. Create a dictionary of fraud-related keywords.
Example:
• Phishing: “phishing,” “email scam,” “fake website.”
• Unauthorized Transactions: “not me,” “unauthorized,” “unknown charge.”
2. Use INDEX or PRXPARSE in SAS to search for these keywords.

data complaints_categorized;
set complaints_clean;
if index(Complaint_Description, 'phishing') > 0 then Fraud_Category = 'Phishing';
else if index(Complaint_Description, 'unauthorized') > 0 then Fraud_Category = 'Unauthorized';
else Fraud_Category = 'Other';
run;

proc freq data=complaints_categorized;
tables Fraud_Category;
run;

7. Build Visual Summaries

Visualize key trends for better understanding.

Examples:
1. Bar Chart of Complaint Types:

proc sgplot data=complaints_categorized;
vbar Fraud_Category / response=Complaint_Count;
title "Complaint Distribution by Fraud Type";
run;

2.	Geographic Hotspots:

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

8. Collaborate and Learn
   • Talk to fraud analysts to understand:
   • How complaints are investigated.
   • Key challenges they face (e.g., false positives, specific fraud types).
   • Read fraud-related case studies or articles to deepen your understanding of the domain.

Would you like me to focus more on SAS code, visualization examples, or fraud types? Let me know!