Task: Table-QA (source dataset: FinQA)

Task: 
You are given a table containing structured data and a natural language question referring to the table. Your goal is to analyze the table and provide an accurate and concise answer based on the given data.

Instructions:
1. Understand the Table: Examine the structure, headers, and data in the table. Identify relevant columns and rows related to the question.
2. Interpret the Question: Determine whether the question requires direct retrieval, aggregation, comparison, sorting, filtering, or inference.
3. Extract the Answer: Use logical reasoning and numerical operations (if necessary) to derive the correct response from the table.
4. Format the Answer: No explanation, return the answer in a structured JSON format: {"answer": <answer>}. If the question cannot be answered due to missing or ambiguous data, return a JSON response with {"answer": null}.

Table:
| industry                                           | otc derivative products ( 1 ) ( dollars in millions )   |
|:---------------------------------------------------|:--------------------------------------------------------|
| utilities                                          | $ 3142                                                  |
| banks and securities firms                         | 2358                                                    |
| funds exchanges and other financial services ( 2 ) | 2433                                                    |
| special purpose vehicles                           | 1908                                                    |
| regional governments                               | 1597                                                    |
| healthcare                                         | 1089                                                    |
| industrials                                        | 914                                                     |
| sovereign governments                              | 816                                                     |
| not-for-profit organizations                       | 672                                                     |
| insurance                                          | 538                                                     |
| real estate                                        | 503                                                     |
| consumer staples                                   | 487                                                     |
| other                                              | 1157                                                    |
| total                                              | $ 17614                                                 |

Question:
did the company have more exposure to the insurance industry than the real estate industry in its derivative portfolio?


pre_text: ( 4 ) cds adjustment represents credit protection purchased from european peripherals 2019 banks on european peripherals 2019 sovereign and financial institution risk .
based on the cds notional amount assuming zero recovery adjusted for any fair value receivable or payable .
( 5 ) represents cds hedges ( purchased and sold ) on net counterparty exposure and funded lending executed by trading desks responsible for hedging counterparty and lending credit risk exposures for the company .
based on the cds notional amount assuming zero recovery adjusted for any fair value receivable or payable .
( 6 ) in addition , at december 31 , 2013 , the company had european peripherals exposure for overnight deposits with banks of approximately $ 111 million .
industry exposure 2014otc derivative products .
the company also monitors its credit exposure to individual industries for current exposure arising from the company 2019s otc derivative contracts .
the following table shows the company 2019s otc derivative products by industry at december 31 , 2013 : industry otc derivative products ( 1 ) ( dollars in millions ) .
post_text: ( 1 ) for further information on derivative instruments and hedging activities , see note 12 to the consolidated financial statements in item 8 .
( 2 ) includes mutual funds , pension funds , private equity and real estate funds , exchanges and clearinghouses and diversified financial services .
operational risk .
operational risk refers to the risk of loss , or of damage to the company 2019s reputation , resulting from inadequate or failed processes , people and systems or from external events ( e.g. , fraud , legal and compliance risks or damage to physical assets ) .
the company may incur operational risk across the full scope of its business activities , including revenue-generating activities ( e.g. , sales and trading ) and control groups ( e.g. , information technology and trade processing ) .
legal , regulatory and compliance risk is included in the scope of operational risk and is discussed below under 201clegal , regulatory and compliance risk . 201d the company has established an operational risk framework to identify , measure , monitor and control risk across the company .
effective operational risk management is essential to reducing the impact of operational risk incidents and mitigating legal , regulatory and reputational risks .
the framework is continually evolving to account for changes in the company and respond to the changing regulatory and business environment .
the company has implemented operational risk data and assessment systems to monitor and analyze internal and external operational risk events , business environment and internal control factors and to perform scenario analysis .
the collected data elements are incorporated in the operational risk capital model .
the model encompasses both quantitative and qualitative elements .
internal loss data and scenario analysis results are direct inputs to the capital model , while external operational incidents , business environment internal control factors and metrics are indirect inputs to the model. .
label: yes
qa: answer: yes
ann_table_rows: 10
11
steps: op: compare_larger1-1
arg1: 538
arg2: 503
res: yes
program: greater(538, 503)
gold_inds: table_10: industry the insurance of otc derivative products ( 1 ) ( dollars in millions ) is 538 ;
table_11: industry the real estate of otc derivative products ( 1 ) ( dollars in millions ) is 503 ;
exe_ans: yes
tfidftopn: text_5: industry exposure 2014otc derivative products .
program_re: greater(538, 503)
model_input: table_10
industry the insurance of otc derivative products ( 1 ) ( dollars in millions ) is 538 ;
table_11
industry the real estate of otc derivative products ( 1 ) ( dollars in millions ) is 503 ;
table_14
industry the total of otc derivative products ( 1 ) ( dollars in millions ) is $ 17614 ;