##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:
A15 refers to number of committed crimes in 1995; A16 refers to number of committed crimes in 1996; status = 'B' means bad loan

##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 account 532, what is the crime trend and ranking of its district, including the percentage change in crimes between 1995 and 1996, how many loans and transactions the account has, and how it compares to the average crime rate across all districts?
Evidence: A15 refers to number of committed crimes in 1995; A16 refers to number of committed crimes in 1996; status = 'B' means bad loan
Answer: Sorry, I cannot answer.
Permission: denied (Missing column permissions: district: A15; account: account_id; client: client_id)
Gold SQL: WITH CrimesByDistrict AS (
    SELECT 
        d.district_id,
        d.A2 AS district_name,
        d.A15 AS crimes_1995,
        d.A16 AS crimes_1996,
        CASE 
            WHEN d.A16 > d.A15 THEN 'Increased'
            WHEN d.A16 < d.A15 THEN 'Decreased'
            ELSE 'Unchanged'
        END AS crime_trend,
        ROUND((d.A16 - d.A15) * 100.0 / d.A15, 2) AS percentage_change,
        RANK() OVER (ORDER BY d.A15 DESC) AS crime_rank_1995,
        RANK() OVER (ORDER BY d.A16 DESC) AS crime_rank_1996
    FROM district d
),
AccountInfo AS (
    SELECT 
        a.account_id,
        a.district_id,
        a.frequency,
        COUNT(DISTINCT l.loan_id) AS loan_count,
        COUNT(DISTINCT t.trans_id) AS transaction_count,
        SUM(CASE WHEN l.status = 'B' THEN 1 ELSE 0 END) AS bad_loans,
        COUNT(DISTINCT c.client_id) AS client_count
    FROM account a
    LEFT JOIN loan l ON a.account_id = l.account_id
    LEFT JOIN trans t ON a.account_id = t.account_id
    LEFT JOIN disp d ON a.account_id = d.account_id
    LEFT JOIN client c ON d.client_id = c.client_id
    WHERE a.account_id = 532
    GROUP BY a.account_id, a.district_id, a.frequency
)
SELECT 
    cd.district_name,
    cd.crimes_1995,
    cd.crimes_1996,
    cd.crime_trend,
    cd.percentage_change,
    cd.crime_rank_1995,
    cd.crime_rank_1996,
    ai.loan_count,
    ai.transaction_count,
    ai.bad_loans,
    ai.client_count,
    (SELECT AVG(d2.A15) FROM district d2) AS avg_crimes_across_districts,
    (SELECT COUNT(*) FROM district WHERE A15 > cd.crimes_1995) AS districts_with_more_crimes
FROM CrimesByDistrict cd
JOIN AccountInfo ai ON cd.district_id = ai.district_id
WHERE EXISTS (
    SELECT 1 
    FROM account a 
    WHERE a.account_id = 532 
    AND a.district_id = cd.district_id
)