##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:
Savings rate is calculated as (total income - total expense) / total income * 100. Age categories are: Young (under 30), Middle-aged (30-50), Senior (over 50). High unemployment area refers to districts with unemployment rate above 1.0%.

##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 demographic, financial, and transaction details of the account owner who has the largest loan amount, including their age category, savings rate, and the economic status of their district?
Evidence: Savings rate is calculated as (total income - total expense) / total income * 100. Age categories are: Young (under 30), Middle-aged (30-50), Senior (over 50). High unemployment area refers to districts with unemployment rate above 1.0%.
Answer: Sorry, I cannot answer.
Permission: denied (Missing column permissions: trans: account, bank, trans_id; disp: account_id, client_id, disp_id, type; client: gender)
Gold SQL: WITH LoanRanking AS (
    SELECT 
        l.account_id,
        l.amount,
        l.loan_id,
        RANK() OVER (ORDER BY l.amount DESC) as loan_rank
    FROM loan l
),
AccountOwners AS (
    SELECT 
        d.client_id,
        d.account_id,
        c.gender,
        c.birth_date,
        CAST(strftime('%Y', 'now') - strftime('%Y', c.birth_date) AS INTEGER) - 
            CASE WHEN strftime('%m-%d', 'now') < strftime('%m-%d', c.birth_date) THEN 1 ELSE 0 END AS age
    FROM disp d
    JOIN client c ON d.client_id = c.client_id
    WHERE d.type = 'OWNER'
),
TransactionSummary AS (
    SELECT 
        account_id,
        COUNT(*) AS transaction_count,
        SUM(CASE WHEN type = 'PRIJEM' THEN amount ELSE 0 END) AS total_income,
        SUM(CASE WHEN type = 'VYDAJ' THEN amount ELSE 0 END) AS total_expense
    FROM trans
    GROUP BY account_id
),
DistrictInfo AS (
    SELECT 
        d.district_id,
        d.A2 AS district_name,
        d.A3 AS region,
        d.A11 AS avg_salary,
        d.A12 AS unemployment_rate
    FROM district d
)
SELECT 
    ao.client_id,
    c.gender,
    ao.age,
    di.district_name,
    di.region,
    lr.amount AS loan_amount,
    lr.loan_id,
    ts.transaction_count,
    ts.total_income,
    ts.total_expense,
    ROUND((ts.total_income - ts.total_expense) * 1.0 / CASE WHEN ts.total_income = 0 THEN 1 ELSE ts.total_income END * 100, 2) AS savings_rate_pct,
    CASE 
        WHEN ao.age < 30 THEN 'Young'
        WHEN ao.age BETWEEN 30 AND 50 THEN 'Middle-aged'
        ELSE 'Senior'
    END AS age_category,
    CASE 
        WHEN di.unemployment_rate > 1.0 THEN 'High unemployment area'
        ELSE 'Low unemployment area'
    END AS area_status
FROM LoanRanking lr
JOIN AccountOwners ao ON lr.account_id = ao.account_id
JOIN client c ON ao.client_id = c.client_id
JOIN DistrictInfo di ON c.district_id = di.district_id
LEFT JOIN TransactionSummary ts ON ao.account_id = ts.account_id
WHERE lr.loan_rank = 1
ORDER BY lr.amount DESC, ao.client_id ASC
LIMIT 1