##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:
District refers to A2; account owners are those with type = 'OWNER'; credit status is determined by loan repayment history where status 'A' indicates good loans and 'B' indicates bad loans; customer priority is based on age and average balance thresholds.

##Role Access Policy (Column-Level):
Role: LoanOfficer
Accessible Columns: account: account_id, district_id, frequency, date; client: client_id, district_id; disp: disp_id, client_id, account_id, type; loan: ALL COLUMNS; trans: account_id, date, type, amount, balance

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 account owners in the Tabor district, provide a comprehensive customer profile including their demographics, transaction history, loan history, credit status, and customer priority ranking based on average balance.
Evidence: District refers to A2; account owners are those with type = 'OWNER'; credit status is determined by loan repayment history where status 'A' indicates good loans and 'B' indicates bad loans; customer priority is based on age and average balance thresholds.
Answer: Sorry, I cannot answer.
Permission: denied (Missing column permissions: trans: trans_id; client: birth_date, gender; card: card_id, disp_id, type; district: A2, district_id)
Gold SQL: WITH EligibleAccounts AS (
    SELECT 
        T2.account_id,
        T2.district_id,
        T2.date AS account_open_date,
        T1.A2 AS district_name,
        T3.client_id
    FROM district AS T1 
    INNER JOIN account AS T2 ON T1.district_id = T2.district_id 
    INNER JOIN disp AS T3 ON T2.account_id = T3.account_id 
    WHERE T3.type = 'OWNER' AND T1.A2 = 'Tabor'
),
AccountTransactionStats AS (
    SELECT 
        t.account_id,
        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,
        AVG(t.balance) AS avg_balance
    FROM trans t
    GROUP BY t.account_id
),
ClientInfo AS (
    SELECT 
        c.client_id,
        c.gender,
        CAST(strftime('%Y', 'now') AS INTEGER) - CAST(strftime('%Y', c.birth_date) AS INTEGER) AS age,
        COUNT(card.card_id) AS card_count,
        GROUP_CONCAT(DISTINCT card.type) AS card_types
    FROM client c
    LEFT JOIN disp d ON c.client_id = d.client_id
    LEFT JOIN card ON d.disp_id = card.disp_id
    GROUP BY c.client_id, c.gender, c.birth_date
),
LoanHistory AS (
    SELECT 
        l.account_id,
        COUNT(l.loan_id) AS loan_count,
        SUM(CASE WHEN l.status = 'A' THEN 1 ELSE 0 END) AS good_loans,
        SUM(CASE WHEN l.status = 'B' THEN 1 ELSE 0 END) AS bad_loans,
        AVG(l.amount) AS avg_loan_amount
    FROM loan l
    GROUP BY l.account_id
)
SELECT 
    ea.account_id,
    ea.district_name,
    ea.account_open_date,
    ci.gender,
    ci.age,
    ci.card_count,
    ci.card_types,
    COALESCE(ats.transaction_count, 0) AS transaction_count,
    COALESCE(ats.total_income, 0) AS total_income,
    COALESCE(ats.total_expense, 0) AS total_expense,
    COALESCE(ats.max_balance, 0) AS max_balance,
    COALESCE(ats.avg_balance, 0) AS avg_balance,
    COALESCE(lh.loan_count, 0) AS previous_loan_count,
    COALESCE(lh.good_loans, 0) AS good_loans,
    COALESCE(lh.bad_loans, 0) AS bad_loans,
    CASE 
        WHEN COALESCE(lh.loan_count, 0) = 0 THEN 'No Loan History'
        WHEN lh.good_loans > lh.bad_loans THEN 'Good Credit History'
        ELSE 'Poor Credit History'
    END AS credit_status,
    CASE
        WHEN ci.age >= 50 AND COALESCE(ats.avg_balance, 0) > 50000 THEN 'High Priority'
        WHEN ci.age BETWEEN 30 AND 49 AND COALESCE(ats.avg_balance, 0) > 30000 THEN 'Medium Priority'
        ELSE 'Standard Priority'
    END AS customer_priority,
    RANK() OVER (ORDER BY COALESCE(ats.avg_balance, 0) DESC) AS balance_rank
FROM EligibleAccounts ea
LEFT JOIN AccountTransactionStats ats ON ea.account_id = ats.account_id
LEFT JOIN ClientInfo ci ON ea.client_id = ci.client_id
LEFT JOIN LoanHistory lh ON ea.account_id = lh.account_id
ORDER BY balance_rank, ea.account_id;