##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:
'POPLATEK PO OBRATU' means statement issued after transaction. District names are in A2, regions in A3, and A10 represents the ratio of urban inhabitants. Transaction types: 'PRIJEM' = income, 'VYDAJ' = expense. Loan status: 'A' = running contract, 'B' = finished contract, 'C' = defaulted loan.

##Role Access Policy (Column-Level):
Role: DataOperator_2
Accessible Columns: account: account_id; card: card_id, disp_id, type; disp: account_id, client_id, disp_id; district: A10, A11, A12, A14, A15, A16, A2, A3, A4, A5, A6, A7, A8, district_id; loan: account_id, amount, duration, status; order: account_id, account_to, amount, k_symbol, order_id; 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 opened in 1993 with statements issued after transactions, provide a comprehensive analysis including district information, transaction activity, client demographics, loan details, and risk assessment.
Evidence: 'POPLATEK PO OBRATU' means statement issued after transaction. District names are in A2, regions in A3, and A10 represents the ratio of urban inhabitants. Transaction types: 'PRIJEM' = income, 'VYDAJ' = expense. Loan status: 'A' = running contract, 'B' = finished contract, 'C' = defaulted loan.
Answer: Sorry, I cannot answer.
Permission: denied (Missing column permissions: district: account_id, client_id, type; account: date, district_id, frequency; client: account_id, avg_client_age, birth_date, client_count, client_id, gender, owner_gender; loan: avg_loan_duration, defaulted_loans, finished_loans, loan_count, loan_id, running_loans, total_loan_amount)
Gold SQL: WITH AccountsIn1993 AS (
    SELECT 
        T1.account_id, 
        T1.district_id,
        T1.date AS account_open_date
    FROM account AS T1 
    WHERE T1.frequency = 'POPLATEK PO OBRATU' 
    AND STRFTIME('%Y', T1.date) = '1993'
),
AccountStats AS (
    SELECT 
        a.account_id,
        COUNT(DISTINCT 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,
        MIN(t.balance) AS min_balance
    FROM AccountsIn1993 a
    LEFT JOIN trans t ON a.account_id = t.account_id
    GROUP BY a.account_id
),
ClientDetails AS (
    SELECT 
        a.account_id,
        COUNT(DISTINCT c.client_id) AS client_count,
        MAX(CASE WHEN d.type = 'OWNER' THEN c.gender ELSE NULL END) AS owner_gender,
        AVG(JULIANDAY(a.account_open_date) - JULIANDAY(c.birth_date))/365.25 AS avg_client_age
    FROM AccountsIn1993 a
    JOIN disp d ON a.account_id = d.account_id
    JOIN client c ON d.client_id = c.client_id
    GROUP BY a.account_id
),
LoanInfo AS (
    SELECT 
        a.account_id,
        COUNT(l.loan_id) AS loan_count,
        SUM(l.amount) AS total_loan_amount,
        AVG(l.duration) AS avg_loan_duration,
        SUM(CASE WHEN l.status = 'A' THEN 1 ELSE 0 END) AS running_loans,
        SUM(CASE WHEN l.status = 'B' THEN 1 ELSE 0 END) AS finished_loans,
        SUM(CASE WHEN l.status = 'C' THEN 1 ELSE 0 END) AS defaulted_loans
    FROM AccountsIn1993 a
    LEFT JOIN loan l ON a.account_id = l.account_id
    GROUP BY a.account_id
)
SELECT 
    a.account_id, 
    d.A2 AS district_name, 
    d.A3 AS district_region,
    CASE 
        WHEN d.A10 > 75 THEN 'Highly Urban'
        WHEN d.A10 > 50 THEN 'Moderately Urban'
        ELSE 'Rural' 
    END AS urbanization_category,
    s.transaction_count,
    ROUND(s.total_income - s.total_expense, 2) AS net_cash_flow,
    s.max_balance - s.min_balance AS balance_volatility,
    c.client_count,
    c.owner_gender,
    ROUND(c.avg_client_age, 1) AS avg_client_age,
    l.loan_count,
    l.total_loan_amount,
    ROUND(l.avg_loan_duration, 1) AS avg_loan_duration_months,
    l.running_loans,
    l.finished_loans,
    l.defaulted_loans,
    CASE WHEN l.defaulted_loans > 0 THEN 'High Risk' 
         WHEN l.loan_count > 0 AND l.defaulted_loans = 0 THEN 'Low Risk'
         ELSE 'No Loans' 
    END AS risk_category
FROM AccountsIn1993 a
INNER JOIN district d ON a.district_id = d.district_id
LEFT JOIN AccountStats s ON a.account_id = s.account_id
LEFT JOIN ClientDetails c ON a.account_id = c.account_id
LEFT JOIN LoanInfo l ON a.account_id = l.account_id
ORDER BY d.A3, d.A2, a.account_id