You are now a sqlite data analyst, and you are given a database schema as follows:

    【Schema】
    【DB_ID】 talkingdata
【Schema】
# Table: label_categories
[
(label_id:INTEGER, Primary Key, Examples: [1, 2, 3]),
(category:TEXT, Examples: [game-game type, game-Game themes, game-Art Style])
]
# Table: gender_age
[
(device_id:INTEGER, Primary Key, Examples: [-9221086586254644858, -9221079146476055829, -9221066489596332354]),
(gender:TEXT, Examples: [M, F]),
(age:INTEGER, Examples: [29, 31, 38]),
(group:TEXT, Examples: [M29-31, M32-38, F29-32])
]
# Table: events_relevant
[
(event_id:INTEGER, Primary Key, Examples: [2, 6, 7]),
(device_id:INTEGER),
(timestamp:DATETIME),
(longitude:REAL, Examples: [1.0]),
(latitude:REAL, Examples: [0.0, 1.0])
]
# Table: gender_age_train
[
(device_id:INTEGER, Primary Key, Examples: [-9223067244542181226, -9222956879900151005, -9222754701995937853]),
(gender:TEXT, Examples: [M, F]),
(age:INTEGER, Examples: [24, 36, 29]),
(group:TEXT, Examples: [M23-26, M32-38, M29-31])
]
# Table: app_all
[
(app_id:INTEGER, Primary Key, Examples: [-9223281467940916832, -9222877069545393219, -9222785464897897681])
]
# Table: app_events_relevant
[
(event_id:INTEGER, Primary Key, Examples: [2, 6, 7]),
(app_id:INTEGER, Primary Key, Examples: [-8942695423876075857, -8022267440849930066, -5720078949152207372]),
(is_installed:INTEGER, Examples: [1]),
(is_active:INTEGER, Examples: [0, 1])
]
# Table: phone_brand_device_model2
[
(device_id:INTEGER, Primary Key, Examples: [-9223321966609553846, -9223067244542181226, -9223042152723782980]),
(phone_brand:TEXT, Primary Key, Examples: [小米, vivo, 三星]),
(device_model:TEXT, Primary Key, Examples: [红米note, Y19T, MI 3])
]
# Table: sample_submission
[
(device_id:INTEGER, Primary Key, Examples: [-9223321966609553846, -9223042152723782980, -9222896629442493034]),
(F23-:REAL, Examples: [0.0833]),
(F24-26:REAL, Examples: [0.0833]),
(F27-28:REAL, Examples: [0.0833]),
(F29-32:REAL, Examples: [0.0833]),
(F33-42:REAL, Examples: [0.0833]),
(F43+:REAL, Examples: [0.0833]),
(M22-:REAL, Examples: [0.0833]),
(M23-26:REAL, Examples: [0.0833]),
(M27-28:REAL, Examples: [0.0833]),
(M29-31:REAL, Examples: [0.0833]),
(M32-38:REAL, Examples: [0.0833]),
(M39+:REAL, Examples: [0.0833])
]
# Table: gender_age_test
[
(device_id:INTEGER, Primary Key, Examples: [-9223321966609553846, -9223042152723782980, -9222896629442493034])
]
# Table: app_events
[
(event_id:INTEGER, Primary Key, Examples: [2, 6, 7]),
(app_id:INTEGER, Primary Key, Examples: [-8942695423876075857, -8022267440849930066, -5720078949152207372]),
(is_installed:INTEGER, Examples: [1]),
(is_active:INTEGER, Examples: [0, 1])
]
# Table: app_labels
[
(app_id:INTEGER, Examples: [7324884708820027918, -4494216993218550286, 6058196446775239644]),
(label_id:INTEGER, Examples: [251, 406, 407])
]
# Table: events
[
(event_id:INTEGER, Primary Key, Examples: [1, 2, 3]),
(device_id:INTEGER, Examples: [29182687948017175, -6401643145415154744, -4833982096941402721]),
(timestamp:DATETIME, Examples: [2016-05-01 00:55:25]),
(longitude:REAL, Examples: [121.0, 104.0, 107.0]),
(latitude:REAL, Examples: [31.0, 30.0, 23.0])
]
【Foreign keys】
gender_age.device_id=phone_brand_device_model2.device_id
events_relevant.device_id=gender_age.device_id
app_events_relevant.app_id=app_all.app_id
app_events_relevant.event_id=events_relevant.event_id
app_events.event_id=events.event_id
app_labels.app_id=app_all.app_id
app_labels.label_id=label_categories.label_id

    【Question】
    How many users belong to "Financial Information" category?

    【Evidence】
    

    Please read and understand the database schema carefully, and generate an executable SQL based on the user's question and evidence. 

SQL: SELECT COUNT(T1.app_id) FROM app_labels AS T1 INNER JOIN label_categories AS T2 ON T1.label_id = T2.label_id WHERE T2.category = 'Financial Information'