Is NLP useful in finance?
Banks can apply natural language processing to large volumes of text and speech data to extract information. In addition, gain insights, streamline manual tasks to accelerate the trades, assess the risks. And lastly, understand the financial sentiment, and construct portfolios while automating auditing and accounting.
Pain points in fixed income monitoring:
• Exponential growth in data volumes
• Fragmented data: FI relies heavily on segregated data disseminated between counterparties. • Liquidity and Regulation: Post-global financial crisis, regulations including Basel III and Volcker Rule caused intermediary costs for banks to be significantly higher.
The NLP approach enables fixed-income monitoring to uncover granular information such as investor behaviors, tendencies, and patterns that enhance decision-making.
Intelligent document Search:
NLP tools ‘read’ hundreds of documents at a time extract information from anywhere and in data sets regardless of the format
For example, JP Morgan Chase’s COIN software uses NLP to help the bank’s legal team search and review large volumes of legal documents. It can extract key data and clauses to help loan officers review commercial loan agreements. The bank claims it extracted 150 relevant attributes from 12,000 commercial credit agreements in seconds.
Risk assessment:
Since the Great Recession, credit rating agencies like Standard & Poor (S&P) and Moody’s have become more erratic in their assignment of upgrades and downgrades in ratings. This volatility is difficult for humans to track and predict. And can lead to more significant swings in corporate and government bond returns. The NLP model draws on data daily and is trained to predict rating changes.
For a retail business, clients’ payment capacity is calculated based on previous spending patterns and past loan payment history data. But this information is not available. So, for example, the ‘cash only’ merchants deprive them of access to bank loans because banks don’t think they can repay, even though they have a large cash flow.
In this case, NLP can measure attitude and an entrepreneurial mindset in business loans. Similarly, it can also point out incoherent data and take it up for more scrutiny. Even more, the subtle aspects like lenders’ and borrowers’ emotions during a loan process can be incorporated with the help of NLP.
Such characteristic leads to another competitive edge of NLP:
Sentiment analysis:
The sentiment analysis in NLP can ‘listen’ calls to determine the tone and sentiment behind what clients are saying, providing insight for analysis.
With NLP, a computer can assess the tone or sentiment of loan clients. For example, are clients using more “negative” words—”losses,” “impairment,” and “adverse” when describing their situation? This may mean they are facing challenges to their business. Word choice and tone can even offer insights into a company’s culture or the leadership style of its executives.
