##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:
Female refers to gender = 'F'; south Bohemia refers to region A3 = 'south Bohemia'; age categories are Young (born after 1980), Middle-aged (born 1960-1980), and Senior (born before 1960); net balance = total income - total expense; active loans refer to loan status = 'A'

##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 and financial profile of female account owners in south Bohemia, broken down by district and age group, including their average transaction activity, net balance, loan amounts, and active loan rates?
Evidence: Female refers to gender = 'F'; south Bohemia refers to region A3 = 'south Bohemia'; age categories are Young (born after 1980), Middle-aged (born 1960-1980), and Senior (born before 1960); net balance = total income - total expense; active loans refer to loan status = 'A'
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 ClientAccounts AS (
    SELECT 
        c.client_id,
        c.gender,
        a.account_id,
        d.A3 AS region,
        d.A2 AS district_name,
        CASE 
            WHEN c.birth_date > '1980-01-01' THEN 'Young'
            WHEN c.birth_date BETWEEN '1960-01-01' AND '1980-01-01' THEN 'Middle-aged'
            ELSE 'Senior'
        END AS age_category,
        a.frequency
    FROM client c
    JOIN district d ON c.district_id = d.district_id
    JOIN disp dp ON c.client_id = dp.client_id
    JOIN account a ON dp.account_id = a.account_id
    WHERE c.gender = 'F' AND d.A3 = 'south Bohemia' AND dp.type = 'OWNER'
),
AccountTransactions AS (
    SELECT 
        ca.client_id,
        ca.account_id,
        ca.district_name,
        ca.age_category,
        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
    FROM ClientAccounts ca
    LEFT JOIN trans t ON ca.account_id = t.account_id
    GROUP BY ca.client_id, ca.account_id, ca.district_name, ca.age_category
),
LoanStatus AS (
    SELECT
        ca.client_id,
        ca.account_id,
        COUNT(l.loan_id) AS loan_count,
        SUM(l.amount) AS total_loan_amount,
        SUM(CASE WHEN l.status = 'A' THEN 1 ELSE 0 END) AS active_loans
    FROM ClientAccounts ca
    LEFT JOIN loan l ON ca.account_id = l.account_id
    GROUP BY ca.client_id, ca.account_id
)
SELECT 
    at.district_name,
    at.age_category,
    COUNT(DISTINCT at.client_id) AS client_count,
    COUNT(DISTINCT at.account_id) AS account_count,
    ROUND(AVG(at.transaction_count), 2) AS avg_transactions_per_account,
    ROUND(AVG(at.total_income - at.total_expense), 2) AS avg_net_balance,
    ROUND(AVG(CASE WHEN ls.loan_count > 0 THEN ls.total_loan_amount / ls.loan_count ELSE 0 END), 2) AS avg_loan_amount,
    SUM(ls.active_loans) AS total_active_loans,
    ROUND(SUM(ls.active_loans) * 100.0 / COUNT(DISTINCT at.client_id), 2) AS active_loans_per_100_clients
FROM AccountTransactions at
LEFT JOIN LoanStatus ls ON at.client_id = ls.client_id AND at.account_id = ls.account_id
GROUP BY at.district_name, at.age_category
ORDER BY at.district_name, 
    CASE 
        WHEN at.age_category = 'Young' THEN 1
        WHEN at.age_category = 'Middle-aged' THEN 2
        ELSE 3
    END;