##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:
Negative balance means balance < 0; credit card withdrawal refers to operation = 'VYBER KARTOU'; monthly issuance refers to frequency = 'POPLATEK MESICNE'; owner age is calculated as of January 1, 2000

##Role Access Policy (Column-Level):
Role: DataOperator_1
Accessible Columns: account: date, district_id, frequency; card: ALL COLUMNS; disp: ALL COLUMNS; district: A10, A13, A14, A16, A2, A5, A6, A7, A9, district_id; loan: account_id, date, loan_id, status; order: account_id, account_to, amount, bank_to, 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 accounts with monthly issuance that went into negative balance after a credit card withdrawal, what are the statistics including average negative balance, maximum withdrawal amount, average number of cards per account, total gold cards, average owner age, and gender distribution of account owners?
Evidence: Negative balance means balance < 0; credit card withdrawal refers to operation = 'VYBER KARTOU'; monthly issuance refers to frequency = 'POPLATEK MESICNE'; owner age is calculated as of January 1, 2000
Answer: Sorry, I cannot answer.
Permission: denied (Missing column permissions: account: account_id, amount, balance, rn; client: birth_date, client_id, gender)
Gold SQL: WITH AccountWithNegativeBalance AS (
    SELECT 
        t.account_id,
        t.date AS transaction_date,
        t.balance,
        t.operation,
        t.amount,
        a.frequency,
        ROW_NUMBER() OVER (PARTITION BY t.account_id ORDER BY t.date DESC) AS rn
    FROM 
        trans t
    INNER JOIN 
        account a ON t.account_id = a.account_id
    WHERE 
        t.balance < 0 
        AND t.operation = 'VYBER KARTOU' 
        AND a.frequency = 'POPLATEK MESICNE'
),
CardUsageStats AS (
    SELECT 
        d.account_id,
        COUNT(c.card_id) AS num_cards,
        MIN(c.issued) AS first_card_issued,
        MAX(c.issued) AS latest_card_issued,
        SUM(CASE WHEN c.type = 'gold' THEN 1 ELSE 0 END) AS gold_cards,
        SUM(CASE WHEN c.type = 'classic' THEN 1 ELSE 0 END) AS classic_cards
    FROM 
        card c
    INNER JOIN 
        disp d ON c.disp_id = d.disp_id
    GROUP BY 
        d.account_id
),
AccountOwnerInfo AS (
    SELECT 
        d.account_id,
        COUNT(DISTINCT cl.client_id) AS num_owners,
        AVG(julianday('2000-01-01') - julianday(cl.birth_date))/365.25 AS avg_owner_age,
        SUM(CASE WHEN cl.gender = 'M' THEN 1 ELSE 0 END) AS male_owners,
        SUM(CASE WHEN cl.gender = 'F' THEN 1 ELSE 0 END) AS female_owners
    FROM 
        disp d
    INNER JOIN 
        client cl ON d.client_id = cl.client_id
    WHERE 
        d.type = 'OWNER'
    GROUP BY 
        d.account_id
)
SELECT 
    COUNT(DISTINCT a.account_id) AS total_accounts_with_negative_balance,
    AVG(a.balance) AS avg_negative_balance,
    MAX(a.amount) AS max_withdrawal_amount,
    AVG(COALESCE(c.num_cards, 0)) AS avg_cards_per_account,
    SUM(COALESCE(c.gold_cards, 0)) AS total_gold_cards,
    AVG(COALESCE(o.avg_owner_age, 0)) AS avg_account_owner_age,
    SUM(CASE WHEN o.male_owners > o.female_owners THEN 1 ELSE 0 END) AS accounts_with_more_male_owners,
    SUM(CASE WHEN o.female_owners > o.male_owners THEN 1 ELSE 0 END) AS accounts_with_more_female_owners,
    SUM(CASE WHEN o.female_owners = o.male_owners AND o.num_owners > 0 THEN 1 ELSE 0 END) AS accounts_with_equal_gender_distribution
FROM 
    AccountWithNegativeBalance a
LEFT JOIN 
    CardUsageStats c ON a.account_id = c.account_id
LEFT JOIN 
    AccountOwnerInfo o ON a.account_id = o.account_id
WHERE 
    a.rn = 1