##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:
Status descriptions: A = Running - OK, B = Running - Issues, C = Finished - No Issues, D = Finished - Issues. Problematic loans are those with status B or D. District information includes A2 for district name, A3 for region, A11 for average salary, and A12 for unemployment rate in 1995.

##Role Access Policy (Column-Level):
Role: ClientServices
Accessible Columns: client: client_id, gender, birth_date, district_id; account: account_id, frequency, date; card: card_id, type, issued; disp: disp_id, account_id, type; district: district_id, A2, A3, A4

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 loan ID 4990, provide a comprehensive profile including the borrower's demographics, loan details with status description, district economic indicators, and how this loan ranks among other loans in the same district.
Evidence: Status descriptions: A = Running - OK, B = Running - Issues, C = Finished - No Issues, D = Finished - Issues. Problematic loans are those with status B or D. District information includes A2 for district name, A3 for region, A11 for average salary, and A12 for unemployment rate in 1995.
Answer: Sorry, I cannot answer.
Permission: denied (Missing column permissions: trans: account_id; disp: client_id; account: district_id; district: A11, A12; loan: account_id, amount, duration, loan_id, status)
Gold SQL: WITH LoanStats AS (
  SELECT 
    l.loan_id,
    l.account_id,
    l.amount,
    l.duration,
    l.status,
    CASE
      WHEN l.status = 'A' THEN 'Running - OK'
      WHEN l.status = 'B' THEN 'Running - Issues'
      WHEN l.status = 'C' THEN 'Finished - No Issues'
      WHEN l.status = 'D' THEN 'Finished - Issues'
      ELSE 'Unknown'
    END AS status_description,
    a.district_id,
    ROUND(l.amount / l.duration, 2) AS avg_monthly_payment,
    RANK() OVER (PARTITION BY a.district_id ORDER BY l.amount DESC) AS district_loan_rank
  FROM loan AS l
  JOIN account AS a ON l.account_id = a.account_id
),
DistrictStats AS (
  SELECT
    d.district_id,
    d.A2 AS district_name,
    d.A3 AS region,
    d.A11 AS avg_salary,
    d.A12 AS unemployment_rate_1995,
    COUNT(DISTINCT l.loan_id) AS total_loans,
    AVG(l.amount) AS avg_loan_amount,
    SUM(CASE WHEN l.status IN ('B', 'D') THEN 1 ELSE 0 END) AS problematic_loans
  FROM district AS d
  LEFT JOIN account AS a ON d.district_id = a.district_id
  LEFT JOIN loan AS l ON a.account_id = l.account_id
  GROUP BY d.district_id, d.A2, d.A3, d.A11, d.A12
)
SELECT
  ls.loan_id,
  d.A2 AS district_name,
  d.A3 AS region,
  c.gender,
  DATE(c.birth_date) AS birth_date,
  ls.amount AS loan_amount,
  ls.duration AS loan_duration_months,
  ls.status_description,
  d.A11 AS district_avg_salary,
  d.A12 AS unemployment_rate_1995,
  ds.total_loans AS district_total_loans,
  ds.avg_loan_amount AS district_avg_loan_amount,
  ds.problematic_loans AS district_problematic_loans,
  ls.district_loan_rank,
  (SELECT COUNT(*) FROM trans AS t WHERE t.account_id = a.account_id) AS total_transactions
FROM LoanStats AS ls
JOIN account AS a ON ls.account_id = a.account_id
JOIN district AS d ON ls.district_id = d.district_id
JOIN DistrictStats AS ds ON d.district_id = ds.district_id
JOIN disp AS dp ON a.account_id = dp.account_id AND dp.type = 'OWNER'
JOIN client AS c ON dp.client_id = c.client_id
WHERE ls.loan_id = 4990