##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:
Transaction income refers to type = 'PRIJEM'; transaction expense refers to type = 'VYDAJ'; good loans refers to status = 'A'; bad loans refers to status = 'B'.

##Role Access Policy (Column-Level):
Role: DataOperator_3
Accessible Columns: account: account_id, date, district_id; card: disp_id, issued, type; client: ALL COLUMNS; disp: account_id, type; district: A10, A11, A14, A16, A2, A3, A4, A5, A6, A7, A8, A9, district_id; loan: account_id, amount, date, duration, loan_id, payments; order: amount, k_symbol; trans: ALL COLUMNS

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: For each account with gold credit cards, provide a comprehensive financial profile including the number of gold cards, location details, transaction history, balance statistics, loan information, and rank the accounts by their average balance.
Evidence: Transaction income refers to type = 'PRIJEM'; transaction expense refers to type = 'VYDAJ'; good loans refers to status = 'A'; bad loans refers to status = 'B'.
Answer: Sorry, I cannot answer.
Permission: denied (Missing column permissions: card: card_id; disp: disp_id; account: frequency; loan: status)
Gold SQL: WITH GoldCardAccounts AS (
    SELECT 
        T2.account_id,
        COUNT(T1.card_id) AS gold_card_count
    FROM disp AS T2  
    INNER JOIN card AS T1 ON T1.disp_id = T2.disp_id  
    WHERE T1.type = 'gold'
    GROUP BY T2.account_id
),
AccountDetails AS (
    SELECT 
        a.account_id,
        a.district_id,
        d.A2 AS district_name,
        d.A3 AS region,
        a.frequency,
        a.date AS account_opening_date,
        JULIANDAY('now') - JULIANDAY(a.date) AS account_age_days
    FROM account a
    JOIN district d ON a.district_id = d.district_id
),
AccountTransactions AS (
    SELECT 
        t.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
    FROM trans t
    GROUP BY t.account_id
),
AccountLoans AS (
    SELECT 
        l.account_id,
        COUNT(l.loan_id) AS loan_count,
        SUM(l.amount) AS total_loan_amount,
        SUM(CASE WHEN l.status = 'A' THEN l.amount ELSE 0 END) AS good_loans,
        SUM(CASE WHEN l.status = 'B' THEN l.amount ELSE 0 END) AS bad_loans
    FROM loan l
    GROUP BY l.account_id
)
SELECT 
    gca.account_id,
    gca.gold_card_count,
    ad.district_name,
    ad.region,
    ad.frequency,
    ad.account_opening_date,
    ROUND(ad.account_age_days/365.25, 2) AS account_age_years,
    COALESCE(at.transaction_count, 0) AS transaction_count,
    COALESCE(at.total_income, 0) AS total_income,
    COALESCE(at.total_expense, 0) AS total_expense,
    COALESCE(at.total_income, 0) - COALESCE(at.total_expense, 0) AS net_flow,
    COALESCE(at.max_balance, 0) AS max_balance,
    COALESCE(at.avg_balance, 0) AS avg_balance,
    COALESCE(al.loan_count, 0) AS loan_count,
    COALESCE(al.total_loan_amount, 0) AS total_loan_amount,
    COALESCE(al.good_loans, 0) AS good_loans,
    COALESCE(al.bad_loans, 0) AS bad_loans,
    CASE 
        WHEN COALESCE(al.loan_count, 0) = 0 THEN 'No Loans'
        WHEN COALESCE(al.bad_loans, 0) > 0 THEN 'Has Bad Loans'
        ELSE 'Good Standing'
    END AS loan_status,
    DENSE_RANK() OVER (ORDER BY COALESCE(at.avg_balance, 0) DESC) AS balance_rank
FROM GoldCardAccounts gca
LEFT JOIN AccountDetails ad ON gca.account_id = ad.account_id
LEFT JOIN AccountTransactions at ON gca.account_id = at.account_id
LEFT JOIN AccountLoans al ON gca.account_id = al.account_id
ORDER BY balance_rank, gca.account_id