##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 statement issuance refers to frequency = 'POPLATEK TYDNE'. Loan status: A = contract finished no problems, B = contract finished loan not paid, C = running contract OK so far, D = running contract client in debt.

##Role Access Policy (Column-Level):
Role: AccountManager
Accessible Columns: account: ALL COLUMNS; card: card_id, disp_id, type, issued; disp: disp_id, client_id, account_id, type; order: ALL COLUMNS; 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 are the statistics for the top 50 highest loan amounts among 24-month loans with weekly statement issuance that have an owner, including total count, average loan amount, payment details, interest rates, loan status distribution, and district economic indicators?
Evidence: Weekly statement issuance refers to frequency = 'POPLATEK TYDNE'. Loan status: A = contract finished no problems, B = contract finished loan not paid, C = running contract OK so far, D = running contract client in debt.
Answer: Sorry, I cannot answer.
Permission: denied (Missing column permissions: disp: A11, A12, A2, A3, district_id; client: client_id; loan: account_id, amount, duration, payments, status)
Gold SQL: WITH LoanAccountStats AS (
    SELECT 
        a.account_id,
        a.district_id,
        a.frequency,
        l.duration,
        l.amount,
        l.payments,
        l.status,
        d.A2 AS district_name,
        d.A3 AS region,
        d.A11 AS avg_salary,
        d.A12 AS unemployment_rate_1995,
        ROUND(l.amount / l.duration, 2) AS monthly_principal,
        ROUND(l.payments - (l.amount / l.duration), 2) AS monthly_interest,
        ROUND(((l.payments * l.duration) - l.amount) / l.amount * 100, 2) AS interest_rate_percent,
        ROW_NUMBER() OVER (PARTITION BY l.duration ORDER BY l.amount DESC) AS amount_rank
    FROM 
        account AS a
        INNER JOIN loan AS l ON a.account_id = l.account_id
        INNER JOIN district AS d ON a.district_id = d.district_id
    WHERE 
        l.duration = 24 
        AND a.frequency = 'POPLATEK TYDNE'
)
SELECT 
    COUNT(las.account_id) AS total_count,
    AVG(las.amount) AS avg_loan_amount,
    MAX(las.amount) AS max_loan_amount,
    MIN(las.amount) AS min_loan_amount,
    AVG(las.payments) AS avg_monthly_payment,
    AVG(las.monthly_interest) AS avg_monthly_interest,
    AVG(las.interest_rate_percent) AS avg_interest_rate,
    SUM(CASE WHEN las.status = 'A' THEN 1 ELSE 0 END) AS status_a_count,
    SUM(CASE WHEN las.status = 'B' THEN 1 ELSE 0 END) AS status_b_count,
    SUM(CASE WHEN las.status = 'C' THEN 1 ELSE 0 END) AS status_c_count,
    SUM(CASE WHEN las.status = 'D' THEN 1 ELSE 0 END) AS status_d_count,
    AVG(las.avg_salary) AS avg_district_salary,
    AVG(las.unemployment_rate_1995) AS avg_unemployment_rate,
    COUNT(DISTINCT las.district_id) AS unique_districts,
    COUNT(DISTINCT las.region) AS unique_regions,
    (SELECT region FROM LoanAccountStats GROUP BY region ORDER BY COUNT(*) DESC LIMIT 1) AS top_region
FROM 
    LoanAccountStats AS las
WHERE 
    las.amount_rank <= 50
    AND EXISTS (
        SELECT 1 
        FROM disp AS d
        JOIN client AS c ON d.client_id = c.client_id
        WHERE d.account_id = las.account_id AND d.type = 'OWNER'
    );