##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:
Gold credit card owners refer to cards with type = 'gold' and disposition type = 'OWNER'. High balance accounts refer to accounts where avg_balance > 1000.

##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 demographic and financial profile of gold credit card owners with account balances averaging over 1,000, broken down by gender?
Evidence: Gold credit card owners refer to cards with type = 'gold' and disposition type = 'OWNER'. High balance accounts refer to accounts where avg_balance > 1000.
Answer: Sorry, I cannot answer.
Permission: denied (Missing column permissions: trans: trans_id; disp: account_id, client_id, disp_id, type; client: gender; card: card_id, disp_id, issued, type)
Gold SQL: WITH gold_owner_cards AS (
    SELECT 
        c.card_id,
        c.disp_id,
        c.type AS card_type,
        c.issued,
        d.account_id,
        d.client_id,
        d.type AS disp_type
    FROM card AS c
    INNER JOIN disp AS d ON c.disp_id = d.disp_id
    WHERE c.type = 'gold' AND d.type = 'OWNER'
),
client_demographics AS (
    SELECT 
        cl.client_id,
        cl.gender,
        strftime('%Y', 'now') - strftime('%Y', cl.birth_date) - 
            (strftime('%m-%d', 'now') < strftime('%m-%d', cl.birth_date)) AS age,
        di.A2 AS district_name,
        di.A3 AS region,
        di.A10 AS urban_ratio,
        di.A11 AS avg_salary
    FROM client AS cl
    JOIN district AS di ON cl.district_id = di.district_id
),
account_activity AS (
    SELECT 
        a.account_id,
        COUNT(t.trans_id) AS transaction_count,
        SUM(CASE WHEN t.type = 'PRIJEM' THEN t.amount ELSE 0 END) AS total_income,
        SUM(CASE WHEN t.type = 'VYDAJ' THEN t.amount ELSE 0 END) AS total_expense,
        MAX(t.balance) AS max_balance,
        AVG(t.balance) AS avg_balance,
        COUNT(DISTINCT l.loan_id) AS loan_count,
        SUM(l.amount) AS total_loan_amount
    FROM account AS a
    LEFT JOIN trans AS t ON a.account_id = t.account_id
    LEFT JOIN loan AS l ON a.account_id = l.account_id
    GROUP BY a.account_id
)

SELECT 
    COUNT(goc.card_id) AS total_gold_owner_cards,
    AVG(cd.age) AS avg_cardholder_age,
    SUM(CASE WHEN cd.gender = 'M' THEN 1 ELSE 0 END) AS male_cardholders,
    SUM(CASE WHEN cd.gender = 'F' THEN 1 ELSE 0 END) AS female_cardholders,
    AVG(cd.avg_salary) AS avg_district_salary,
    AVG(aa.transaction_count) AS avg_transactions_per_account,
    AVG(aa.total_income) AS avg_income_per_account,
    AVG(aa.total_expense) AS avg_expense_per_account,
    SUM(CASE WHEN aa.loan_count > 0 THEN 1 ELSE 0 END) AS accounts_with_loans,
    COUNT(DISTINCT cd.region) AS distinct_regions,
    (SELECT COUNT(*) FROM card WHERE type = 'gold') AS total_gold_cards,
    ROUND(COUNT(goc.card_id) * 100.0 / (SELECT COUNT(*) FROM card WHERE type = 'gold'), 2) AS gold_owner_percentage
FROM gold_owner_cards AS goc
JOIN client_demographics AS cd ON goc.client_id = cd.client_id
JOIN account_activity AS aa ON goc.account_id = aa.account_id
WHERE aa.avg_balance > 1000
GROUP BY cd.gender
ORDER BY avg_cardholder_age DESC;