##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:
Active loans refers to status = 'A'. Young borrower means age at card issue is less than 30 years old with at least one loan. Mature borrower means age at card issue is 30 or older with at least one loan.

##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: For all clients who received their credit card on March 3rd, 1994, provide a comprehensive profile including their personal information, banking activity, loan history, and district characteristics. Categorize them as borrowers based on their age and loan status, and rank them by age within their gender group.
Evidence: Active loans refers to status = 'A'. Young borrower means age at card issue is less than 30 years old with at least one loan. Mature borrower means age at card issue is 30 or older with at least one loan.
Answer: Sorry, I cannot answer.
Permission: denied (Missing column permissions: client: gender; card: disp_id, issued, type)
Gold SQL: WITH ClientCardInfo AS (
    SELECT 
        c.client_id,
        c.gender,
        c.birth_date,
        cd.issued,
        cd.type AS card_type,
        STRFTIME('%Y', c.birth_date) AS birth_year,
        STRFTIME('%Y', cd.issued) - STRFTIME('%Y', c.birth_date) AS age_at_card_issue
    FROM client c
    JOIN disp d ON c.client_id = d.client_id
    JOIN card cd ON d.disp_id = cd.disp_id
    WHERE cd.issued = '1994-03-03'
),
ClientAccountInfo AS (
    SELECT 
        c.client_id,
        COUNT(DISTINCT a.account_id) AS account_count,
        SUM(CASE WHEN l.loan_id IS NOT NULL THEN 1 ELSE 0 END) AS loan_count,
        SUM(CASE WHEN l.status = 'A' THEN 1 ELSE 0 END) AS active_loans,
        AVG(CASE WHEN l.loan_id IS NOT NULL THEN l.amount ELSE NULL END) AS avg_loan_amount
    FROM client c
    JOIN disp d ON c.client_id = d.client_id
    JOIN account a ON d.account_id = a.account_id
    LEFT JOIN loan l ON a.account_id = l.account_id
    GROUP BY c.client_id
),
ClientDistrictInfo AS (
    SELECT 
        c.client_id,
        d.A2 AS district_name,
        d.A3 AS region,
        d.A11 AS avg_salary,
        d.A14 AS entrepreneurs_per_1000
    FROM client c
    JOIN district d ON c.district_id = d.district_id
)

SELECT 
    ci.client_id,
    ci.gender,
    ci.birth_date,
    ci.card_type,
    ci.age_at_card_issue,
    cai.account_count,
    cai.loan_count,
    cai.active_loans,
    cai.avg_loan_amount,
    cdi.district_name,
    cdi.region,
    cdi.avg_salary,
    CASE 
        WHEN cai.loan_count > 0 AND ci.age_at_card_issue < 30 THEN 'Young borrower'
        WHEN cai.loan_count > 0 AND ci.age_at_card_issue >= 30 THEN 'Mature borrower'
        ELSE 'Non-borrower'
    END AS borrower_category,
    RANK() OVER (PARTITION BY ci.gender ORDER BY ci.age_at_card_issue) AS age_rank_by_gender
FROM ClientCardInfo ci
JOIN ClientAccountInfo cai ON ci.client_id = cai.client_id
JOIN ClientDistrictInfo cdi ON ci.client_id = cdi.client_id
ORDER BY ci.client_id;