##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:
Post-transaction issuance refers to frequency = 'POPLATEK PO OBRATU'. Disponent refers to disposition type = 'DISPONENT'. Status 'A' indicates active loans while 'B' indicates completed loans.

##Role Access Policy (Column-Level):
Role: DataOperator_1
Accessible Columns: account: date, district_id, frequency; card: ALL COLUMNS; disp: ALL COLUMNS; district: A10, A13, A14, A16, A2, A5, A6, A7, A9, district_id; loan: account_id, date, loan_id, status; order: account_id, account_to, amount, bank_to, k_symbol; 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 all disponent clients with accounts that have post-transaction issuance statements, provide a comprehensive profile including their loan statistics, transaction activity, credit card information, and categorize them based on their service usage, ranked by transaction volume.
Evidence: Post-transaction issuance refers to frequency = 'POPLATEK PO OBRATU'. Disponent refers to disposition type = 'DISPONENT'. Status 'A' indicates active loans while 'B' indicates completed loans.
Answer: Sorry, I cannot answer.
Permission: denied (Missing column permissions: district: A3, account_id, client_id, disp_id, type; account: account_id; client: birth_date, card_id, client_id, disp_id, district_id, gender, type; loan: amount)
Gold SQL: WITH ClientLoanInfo AS (
    SELECT 
        d.client_id,
        d.type AS disposition_type,
        a.frequency,
        COUNT(l.loan_id) AS loan_count,
        AVG(l.amount) AS avg_loan_amount,
        SUM(CASE WHEN l.status = 'A' THEN 1 ELSE 0 END) AS active_loans,
        SUM(CASE WHEN l.status = 'B' THEN 1 ELSE 0 END) AS completed_loans
    FROM disp d
    JOIN account a ON d.account_id = a.account_id
    LEFT JOIN loan l ON a.account_id = l.account_id
    WHERE a.frequency = 'POPLATEK PO OBRATU' AND d.type = 'DISPONENT'
    GROUP BY d.client_id, d.type, a.frequency
),
ClientTransactions AS (
    SELECT 
        d.client_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.date) AS last_transaction_date
    FROM disp d
    JOIN account a ON d.account_id = a.account_id
    JOIN trans t ON a.account_id = t.account_id
    WHERE a.frequency = 'POPLATEK PO OBRATU' AND d.type = 'DISPONENT'
    GROUP BY d.client_id
),
ClientCards AS (
    SELECT
        d.client_id,
        COUNT(c.card_id) AS card_count,
        GROUP_CONCAT(DISTINCT c.type) AS card_types
    FROM disp d
    JOIN account a ON d.account_id = a.account_id
    LEFT JOIN card c ON d.disp_id = c.disp_id
    WHERE a.frequency = 'POPLATEK PO OBRATU' AND d.type = 'DISPONENT'
    GROUP BY d.client_id
)
SELECT 
    cli.client_id,
    c.gender,
    c.birth_date,
    d.A2 AS district_name,
    d.A3 AS region,
    cli.loan_count,
    cli.avg_loan_amount,
    cli.active_loans,
    cli.completed_loans,
    ct.transaction_count,
    ct.total_income,
    ct.total_expense,
    ct.total_income - ct.total_expense AS net_balance,
    ct.last_transaction_date,
    cc.card_count,
    cc.card_types,
    CASE 
        WHEN cli.loan_count > 0 AND cc.card_count > 0 THEN 'Full Service Client'
        WHEN cli.loan_count > 0 THEN 'Loan Only Client'
        WHEN cc.card_count > 0 THEN 'Card Only Client'
        ELSE 'Basic Client'
    END AS client_category,
    RANK() OVER (ORDER BY ct.transaction_count DESC) AS transaction_rank
FROM ClientLoanInfo cli
JOIN client c ON cli.client_id = c.client_id
JOIN district d ON c.district_id = d.district_id
LEFT JOIN ClientTransactions ct ON cli.client_id = ct.client_id
LEFT JOIN ClientCards cc ON cli.client_id = cc.client_id
ORDER BY transaction_rank, cli.client_id;