In striving for business advantage, financial institutions increasingly are leveraging a technology that is growing in power and flexibility: natural language processing, or NLP.
A field of Artificial Intelligence and Linguistics, NLP enables computers to understand and use natural language to communicate with humans. The potential of Machine Learning (ML) algorithms has been unleashed by the vast increase in computational capabilities, this has contributed extensively towards making NLP much more scalable and reliable.
In a field like financial services where speed and accuracy are often requirements for competitive advantage, NLP is especially useful because it can help professionals make better decisions faster. Here are four areas where NLP is already thriving:
Credit Checks: NLP can be used in tandem with credit scoring software to determine the credit worthiness of a customer. It extracts relevant data from documents such as loan applications, income/expenses statements, investments etc., which are then analyzed by credit scoring software to determine a credit score.
Reporting and Auditing: NLP in combination with Machine Learning algorithms can identify significant data elements in unstructured financial statements, purchase orders, invoices, or other payment documentation. Similar functionality can be used in financial auditing, where financial reports are analyzed and classified for anomalies or deviations.
Anti-Fraud: NLP combined with Machine Learning and predictive analytics can be used to detect fraud and misinterpreted information. For example, in using NLP for Know Your Customer (KYC) and Anti-Money Laundering (AML) applications, financial institutions extract and analyze data from a customer’s personal details, spending habits, and financial behavior to reduce fraudulent activities. Similarly, in conjunction with a robotic process automation solution, NLP can be used to review and read submitted documents to identify fraudulent claims.
Customer Service: Most financial institutions deploy NLP for both voice and chatbot applications to help answer customers’ routine queries. In addition, these interactions are analyzed using sentiment analysis tools to predict customer needs and pain points.
To get started with NLP, companies can follow five helpful steps:
- Identify your business goals: What is the business challenge you need quickly resolved? It can be macro or micro; customer-facing or internal- — know your problem to solve it efficiently.
- Data compilation: Pull together all the vast sources of data. These can be log files, transcriptions, emails, feedback, etc. It needs to be prepared for the model to give you the desired outcome.
- Feature engineering: Select and transform the raw data into features that can be easier to interpret by the model.
- Train, test, optimize: Train the model, validate it on the test data set, and adjust and perfect the output.
- Deploy: Once ready, the model can now be integrated into systems like ERP or CRM to start generating results. Remember, NLP deployment does not follow a one-size-fits-all formula. Organizations can customize a model to fit their unique needs.
The acceleration of NLP adoption in the last couple of years has been driven by evolving customer expectations and companies’ digital transformation efforts. As NLP technology advances, large-scale deployments across the enterprise will become increasingly common and those enterprises that use humans to augment the AI technology can have a competitive advantage.
Source: cio.techgig.com
