##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:
Average salary refers to A11; Female refers to gender = 'F'; Loan status: 'A' = active, 'B' = completed, 'C' = defaulted

##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 are the overall statistics for female clients in districts where the average salary is between 6,000 and 10,000, ranking in the top 3 for salary within their region, having at least 5 female clients, and with active loan accounts?
Evidence: Average salary refers to A11; Female refers to gender = 'F'; Loan status: 'A' = active, 'B' = completed, 'C' = defaulted
Answer: Sorry, I cannot answer.
Permission: denied (Missing column permissions: disp: A11, A2, A3, account_id, client_id, district_id; client: gender)
Gold SQL: WITH DistrictStats AS (
    SELECT 
        d.district_id,
        d.A2 AS district_name,
        d.A3 AS region,
        d.A11 AS avg_salary,
        COUNT(DISTINCT c.client_id) AS female_clients,
        AVG(CAST(strftime('%Y', 'now') - strftime('%Y', c.birth_date) AS INTEGER)) AS avg_age,
        RANK() OVER (PARTITION BY d.A3 ORDER BY d.A11 DESC) AS salary_rank_in_region
    FROM 
        district d
    JOIN 
        client c ON d.district_id = c.district_id
    WHERE 
        c.gender = 'F'
    GROUP BY 
        d.district_id, d.A2, d.A3, d.A11
    HAVING 
        d.A11 BETWEEN 6000 AND 10000
),
AccountActivity AS (
    SELECT 
        a.district_id,
        COUNT(DISTINCT a.account_id) AS total_accounts,
        COUNT(DISTINCT l.loan_id) AS total_loans,
        ROUND(AVG(l.amount), 2) AS avg_loan_amount,
        SUM(CASE WHEN l.status = 'A' THEN 1 ELSE 0 END) AS active_loans,
        SUM(CASE WHEN l.status = 'B' THEN 1 ELSE 0 END) AS completed_loans,
        SUM(CASE WHEN l.status = 'C' THEN 1 ELSE 0 END) AS defaulted_loans
    FROM 
        account a
    LEFT JOIN 
        loan l ON a.account_id = l.account_id
    JOIN 
        disp d ON a.account_id = d.account_id
    JOIN 
        client c ON d.client_id = c.client_id
    WHERE 
        c.gender = 'F'
    GROUP BY 
        a.district_id
)
SELECT 
    COUNT(DISTINCT ds.district_id) AS district_count,
    SUM(ds.female_clients) AS total_female_clients,
    ROUND(AVG(ds.avg_salary), 2) AS average_female_salary,
    ROUND(AVG(ds.avg_age), 1) AS average_female_age,
    SUM(aa.total_accounts) AS total_female_accounts,
    SUM(aa.total_loans) AS total_female_loans,
    ROUND(AVG(aa.avg_loan_amount), 2) AS average_female_loan_amount,
    SUM(aa.active_loans) AS active_female_loans,
    SUM(aa.completed_loans) AS completed_female_loans,
    SUM(aa.defaulted_loans) AS defaulted_female_loans,
    GROUP_CONCAT(DISTINCT ds.region) AS regions_represented
FROM 
    DistrictStats ds
JOIN 
    AccountActivity aa ON ds.district_id = aa.district_id
WHERE 
    ds.salary_rank_in_region <= 3
    AND ds.female_clients >= 5
    AND aa.total_loans > 0;