##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:
Weekly statements refers to frequency = 'POPLATEK TYDNE'; age groups are defined as Young (under 30), Middle-aged (30-50), and Senior (over 50); PRIJEM represents income transactions and VYDAJ represents expense transactions.

##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: What is the demographic breakdown and financial profile of account owners who receive weekly statements, segmented by gender, age group, and region?
Evidence: Weekly statements refers to frequency = 'POPLATEK TYDNE'; age groups are defined as Young (under 30), Middle-aged (30-50), and Senior (over 50); PRIJEM represents income transactions and VYDAJ represents expense transactions.
Answer: Sorry, I cannot answer.
Permission: denied (Missing column permissions: district: A3; account: account_id; client: birth_date, client_id, district_id, gender; loan: amount)
Gold SQL: WITH CustomerWeeklyStatements AS (
    SELECT 
        T2.client_id,
        T1.account_id,
        T1.district_id,
        T1.date AS account_open_date,
        COUNT(DISTINCT T3.card_id) AS num_cards
    FROM 
        account AS T1
    INNER JOIN 
        disp AS T2 ON T1.account_id = T2.account_id
    LEFT JOIN
        card AS T3 ON T2.disp_id = T3.disp_id
    WHERE 
        T2.type = 'OWNER' 
        AND T1.frequency = 'POPLATEK TYDNE'
    GROUP BY
        T2.client_id, T1.account_id, T1.district_id, T1.date
),
ClientInfo AS (
    SELECT 
        c.client_id,
        c.gender,
        c.birth_date,
        CASE
            WHEN strftime('%Y', 'now') - strftime('%Y', c.birth_date) - (strftime('%m-%d', 'now') < strftime('%m-%d', c.birth_date)) < 30 THEN 'Young'
            WHEN strftime('%Y', 'now') - strftime('%Y', c.birth_date) - (strftime('%m-%d', 'now') < strftime('%m-%d', c.birth_date)) BETWEEN 30 AND 50 THEN 'Middle-aged'
            ELSE 'Senior'
        END AS age_group,
        d.A2 AS district_name,
        d.A3 AS region
    FROM 
        client c
    JOIN 
        district d ON c.district_id = d.district_id
),
LoanAndTransactionData AS (
    SELECT 
        cws.client_id,
        COUNT(DISTINCT l.loan_id) AS num_loans,
        COALESCE(SUM(l.amount), 0) AS total_loan_amount,
        COALESCE(MAX(l.amount), 0) AS max_loan_amount,
        COUNT(DISTINCT t.trans_id) AS num_transactions,
        COALESCE(SUM(CASE WHEN t.type = 'PRIJEM' THEN t.amount ELSE 0 END), 0) AS total_income,
        COALESCE(SUM(CASE WHEN t.type = 'VYDAJ' THEN t.amount ELSE 0 END), 0) AS total_expense
    FROM 
        CustomerWeeklyStatements cws
    LEFT JOIN 
        loan l ON cws.account_id = l.account_id
    LEFT JOIN 
        trans t ON cws.account_id = t.account_id
    GROUP BY 
        cws.client_id
)

SELECT 
    COUNT(DISTINCT cws.client_id) AS total_weekly_owners,
    ci.gender,
    ci.age_group,
    ci.region,
    ROUND(AVG(cws.num_cards), 2) AS avg_cards_per_customer,
    ROUND(AVG(ltd.num_loans), 2) AS avg_loans_per_customer,
    ROUND(AVG(ltd.total_loan_amount), 2) AS avg_loan_amount,
    ROUND(AVG(ltd.num_transactions), 2) AS avg_transactions,
    ROUND(AVG(ltd.total_income - ltd.total_expense), 2) AS avg_net_balance,
    COUNT(DISTINCT CASE WHEN ltd.num_loans > 0 THEN cws.client_id END) AS customers_with_loans,
    ROUND(COUNT(DISTINCT CASE WHEN ltd.num_loans > 0 THEN cws.client_id END) * 100.0 / COUNT(DISTINCT cws.client_id), 2) AS percent_with_loans
FROM 
    CustomerWeeklyStatements cws
JOIN 
    ClientInfo ci ON cws.client_id = ci.client_id
JOIN 
    LoanAndTransactionData ltd ON cws.client_id = ltd.client_id
GROUP BY 
    ci.gender, ci.age_group, ci.region
ORDER BY 
    total_weekly_owners DESC;