##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:
Male refers to gender = 'M'; A15 refers to number of committed crimes in 1995; Age is calculated from birth_date to current date

##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 the district with the second-highest number of crimes committed in 1995, provide a comprehensive breakdown of all male clients including their total count, the crime count for that year, total accounts, loans, credit cards, average dispositions per client, number of clients with loans, and age distribution across young (under 30), middle-aged (30-50), and senior (over 50) categories.
Evidence: Male refers to gender = 'M'; A15 refers to number of committed crimes in 1995; Age is calculated from birth_date to current date
Answer: Sorry, I cannot answer.
Permission: denied (Missing column permissions: client: gender; card: card_id, disp_id)
Gold SQL: WITH CrimeRanking AS (
    SELECT 
        district_id,
        A15,
        RANK() OVER (ORDER BY A15 DESC) as crime_rank
    FROM district
),
SecondHighestCrimeDistrict AS (
    SELECT district_id, A15
    FROM CrimeRanking
    WHERE crime_rank = 2
),
ClientStats AS (
    SELECT 
        c.client_id,
        c.gender,
        c.district_id,
        COUNT(DISTINCT d.disp_id) as num_dispositions,
        COUNT(DISTINCT a.account_id) as num_accounts,
        COUNT(DISTINCT l.loan_id) as num_loans,
        COUNT(DISTINCT card.card_id) as num_cards,
        CASE 
            WHEN strftime('%Y', 'now') - strftime('%Y', c.birth_date) - 
                (strftime('%m-%d', 'now') < strftime('%m-%d', c.birth_date)) < 30 THEN 'Young'
            WHEN strftime('%Y', 'now') - strftime('%Y', c.birth_date) - 
                (strftime('%m-%d', 'now') < strftime('%m-%d', c.birth_date)) BETWEEN 30 AND 50 THEN 'Middle-aged'
            ELSE 'Senior'
        END as age_category
    FROM client c
    LEFT JOIN disp d ON c.client_id = d.client_id
    LEFT JOIN account a ON d.account_id = a.account_id
    LEFT JOIN loan l ON a.account_id = l.account_id
    LEFT JOIN card ON d.disp_id = card.disp_id
    WHERE c.gender = 'M'
    GROUP BY c.client_id, c.gender, c.district_id
)
SELECT 
    COUNT(cs.client_id) as total_male_clients,
    shcd.A15 as crime_count_1995,
    SUM(cs.num_accounts) as total_accounts,
    SUM(cs.num_loans) as total_loans,
    SUM(cs.num_cards) as total_cards,
    ROUND(AVG(cs.num_dispositions), 2) as avg_dispositions_per_client,
    SUM(CASE WHEN cs.num_loans > 0 THEN 1 ELSE 0 END) as clients_with_loans,
    SUM(CASE WHEN cs.age_category = 'Young' THEN 1 ELSE 0 END) as young_clients,
    SUM(CASE WHEN cs.age_category = 'Middle-aged' THEN 1 ELSE 0 END) as middle_aged_clients,
    SUM(CASE WHEN cs.age_category = 'Senior' THEN 1 ELSE 0 END) as senior_clients
FROM ClientStats cs
JOIN SecondHighestCrimeDistrict shcd ON cs.district_id = shcd.district_id
JOIN district d ON cs.district_id = d.district_id
WHERE cs.gender = 'M'
GROUP BY cs.district_id, d.A2, shcd.A15