You work with insurance policy-term loss data. For a given policy term, its **ultimate loss** is defined as the sum of losses from the current term and all future terms of the same policy, ordered by term sequence. Assume the following table: ```sql CREATE TABLE policy_term_losses ( policy_id VARCHAR, -- Unique identifier for a policy term_id VARCHAR, -- Unique identifier for a policy term term_sequence INTEGER, -- Sequence number of the term within the policy; larger means later term term_start_date DATE, term_end_date DATE, loss_amount DECIMAL(18, 2) -- Loss incurred during this policy term ); ``` Assumptions: - Each `term_id` belongs to exactly one `policy_id`. - `term_sequence` determines the chronological order of terms within a policy. - There is at most one row per `(policy_id, term_sequence)`. - `loss_amount` can be zero, positive, or negative due to adjustments. - No timezone conversion is required because the calculation is based on term sequence, not timestamps. Task: Write a SQL query that returns the ultimate loss for each policy term. Required output columns: - `policy_id` - `term_id` - `term_sequence` - `loss_amount` - `ultimate_loss`, defined as the sum of `loss_amount` for the current row and all rows from the same `policy_id` with `term_sequence >= current term_sequence`. Example: If policy `P1` has term losses by sequence: | policy_id | term_id | term_sequence | loss_amount | |---|---:|---:|---:| | P1 | T1 | 1 | 100 | | P1 | T2 | 2 | 50 | | P1 | T3 | 3 | 25 | Then the expected ultimate losses are: | policy_id | term_id | term_sequence | loss_amount | ultimate_loss | |---|---:|---:|---:|---:| | P1 | T1 | 1 | 100 | 175 | | P1 | T2 | 2 | 50 | 75 | | P1 | T3 | 3 | 25 | 25 |