##Instruction:
Database: financial
Total Tables: 8
Total Columns: 55

Table Schemas:

Database: financial
Total Tables: 8
Total Columns: 55

Table Schemas:

Table: account (4 columns)
--------------------------
• account_id (integer): the id of the account
• district_id (integer): location of branch
• frequency (text): frequency of the acount
• date (date): the creation date of the account
  Values: in the form YYMMDD

Table: card (4 columns)
-----------------------
• card_id (integer): id number of credit card
• disp_id (integer): disposition id
• type (text): type of credit card
  Values: "junior": junior class of credit card; 
"classic": standard class of credit card; 
"gold": high-leve...
• issued (date): the date when the credit card issued
  Values: in the form YYMMDD

Table: client (4 columns)
-------------------------
• client_id (integer): the unique number
• gender (text)
  Values: F：female 
M：male
• birth_date (date): birth date
• district_id (integer): location of branch

Table: disp (4 columns)
-----------------------
• disp_id (integer): unique number of identifying this row of record
• client_id (integer): id number of client
• account_id (integer): id number of account
• type (text): type of disposition
  Values: "OWNER" : "USER" : "DISPONENT"
commonsense evidence:
the account can only have the right to issue pe...

Table: district (16 columns)
----------------------------
• district_id (integer): location of branch
• A2 (text): district_name
• A3 (text): region
• A4 (text)
• A5 (text): municipality < district < region
• A6 (text): municipality < district < region
• A7 (text): municipality < district < region
• A8 (integer): municipality < district < region
• A9 (integer)
  Values: not useful
• A10 (real): ratio of urban inhabitants
• A11 (integer): average salary
• A12 (real): unemployment rate 1995
• A13 (real): unemployment rate 1996
• A14 (integer): no. of entrepreneurs per 1000 inhabitants
• A15 (integer): no. of committed crimes 1995
• A16 (integer): no. of committed crimes 1996

Table: loan (7 columns)
-----------------------
• loan_id (integer): the id number identifying the loan data
• account_id (integer): the id number identifying the account
• date (date): the date when the loan is approved
• amount (integer): approved amount
  Values: unit：US dollar
• duration (integer): loan duration
  Values: unit：month
• payments (real): monthly payments
  Values: unit：month
• status (text): repayment status
  Values: 'A' stands for contract finished, no problems;
'B' stands for contract finished, loan not paid;
'C' ...

Table: order (6 columns)
------------------------
• order_id (integer): identifying the unique order
• account_id (integer): id number of account
• bank_to (text): bank of the recipient
• account_to (integer): account of the recipient
  Values: each bank has unique two-letter code
• amount (real): debited amount
• k_symbol (text): purpose of the payment
  Values: "POJISTNE" stands for insurance payment
"SIPO" stands for household payment
"LEASING" stands for lea...

Table: trans (10 columns)
-------------------------
• trans_id (integer): transaction id
• account_id (integer)
• date (date): date of transaction
• type (text): +/- transaction
  Values: "PRIJEM" stands for credit
"VYDAJ" stands for withdrawal
• operation (text): mode of transaction
  Values: "VYBER KARTOU": credit card withdrawal
"VKLAD": credit in cash
"PREVOD Z UCTU" :collection from anot...
• amount (integer): amount of money
  Values: Unit：USD
• balance (integer): balance after transaction
  Values: Unit：USD
• k_symbol (text)
  Values: "POJISTNE": stands for insurrance payment
"SLUZBY": stands for payment for statement
"UROK": stands ...
• bank (text)
  Values: each bank has unique two-letter code
• account (integer)

##Evidence:
Credit card transactions refer to operation = 'VYBER KARTOU'. Only account owners with credit cards are considered.

##Role Access Policy (Column-Level):
Role: RiskAnalyst
Accessible Columns: account: account_id, district_id, frequency; client: client_id, birth_date, district_id; district: ALL COLUMNS; loan: loan_id, account_id, date, amount, duration, payments, status; trans: account_id, date, type, operation, amount, balance, k_symbol

Based on the above schema and your role's column-level access policy, generate a SQL query to answer the question. If your role does not have access to the required columns, respond with: "Sorry, I cannot answer."


Question: What is the breakdown of credit card transaction patterns in 1998 by region, district, gender, and card type, including average transaction amounts, total spending, and how these amounts compare to district average salaries?
Evidence: Credit card transactions refer to operation = 'VYBER KARTOU'. Only account owners with credit cards are considered.
Answer: Sorry, I cannot answer.
Permission: denied (Missing column permissions: trans: bank; client: gender; card: disp_id, issued, type)
Gold SQL: WITH CardHolders AS (
    SELECT 
        c.client_id,
        c.gender,
        d.disp_id,
        d.account_id,
        a.district_id,
        cd.type AS card_type,
        cd.issued AS card_issue_date
    FROM client c
    JOIN disp d ON c.client_id = d.client_id
    JOIN card cd ON d.disp_id = cd.disp_id
    JOIN account a ON d.account_id = a.account_id
    WHERE d.type = 'OWNER'
),
TransactionStats AS (
    SELECT 
        t.account_id,
        ch.client_id,
        ch.gender,
        ch.card_type,
        STRFTIME('%Y', t.date) AS trans_year,
        STRFTIME('%m', t.date) AS trans_month,
        t.amount,
        t.operation,
        t.balance,
        t.k_symbol,
        t.bank,
        ROW_NUMBER() OVER (PARTITION BY t.account_id ORDER BY t.date) AS transaction_sequence,
        AVG(t.amount) OVER (PARTITION BY t.account_id) AS avg_transaction_amount,
        SUM(t.amount) OVER (PARTITION BY t.account_id) AS total_spent,
        COUNT(*) OVER (PARTITION BY t.account_id) AS transaction_count
    FROM trans t
    JOIN CardHolders ch ON t.account_id = ch.account_id
    WHERE t.operation = 'VYBER KARTOU'
    AND STRFTIME('%Y', t.date) = '1998'
),
DistrictInfo AS (
    SELECT 
        d.district_id,
        d.A2 AS district_name,
        d.A3 AS region,
        CAST(d.A11 AS INTEGER) AS avg_salary,
        CAST(d.A10 AS REAL) AS urban_ratio
    FROM district d
)

SELECT 
    di.region,
    di.district_name,
    ts.gender,
    ts.card_type,
    COUNT(DISTINCT ts.client_id) AS num_clients,
    COUNT(DISTINCT ts.account_id) AS num_accounts,
    ROUND(AVG(ts.amount), 2) AS avg_transaction_amount,
    ROUND(SUM(ts.amount), 2) AS total_amount,
    ROUND(MAX(ts.amount), 2) AS max_transaction,
    ROUND(MIN(ts.amount), 2) AS min_transaction,
    AVG(ts.transaction_count) AS avg_transactions_per_account,
    ROUND(AVG(CASE WHEN ts.gender = 'M' THEN ts.amount ELSE NULL END), 2) AS male_avg_amount,
    ROUND(AVG(CASE WHEN ts.gender = 'F' THEN ts.amount ELSE NULL END), 2) AS female_avg_amount,
    ROUND(AVG(di.avg_salary), 2) AS district_avg_salary,
    ROUND(AVG(ts.amount) / AVG(di.avg_salary) * 100, 2) AS pct_of_avg_salary
FROM TransactionStats ts
JOIN CardHolders ch ON ts.account_id = ch.account_id
JOIN DistrictInfo di ON ch.district_id = di.district_id
GROUP BY di.region, di.district_name, ts.gender, ts.card_type
HAVING COUNT(ts.amount) > 0
ORDER BY avg_transaction_amount DESC;