Empowering Banking with Natural Language Processing (NLP)

Thomas Mathew

Introduction In the digital era, the banking industry faces ever-increasing volumes of data, customer inquiries, and regulatory requirements. To navigate this landscape effectively, a number of banks and financial institutions have started using advanced technologies such as Natural Language Processing (NLP). NLP has the potential to transform banking operations, enhance customer experiences, and drive innovation.

What is NLP?

NLP is a branch of Artificial Intelligence (AI) that focuses on computers and human language interaction. It helps computers to recognise, interpret, and generate human language meaningfully and usefully. It encompasses various tasks and techniques that involve the processing and analysis of natural language.

NLP techniques involve the use of statistical models, Machine Learning algorithms, and linguistic rules to analyse and understand the structure and meaning of language. These techniques include tokenisation (breaking text into smaller units like words or sentences), part-ofspeech tagging (labelling words with their grammatical categories), syntactic parsing (analysing the grammatical structure of sentences), and semantic analysis (extracting meaning from text).

NLP has numerous applications, including chatbots, virtual assistants, information retrieval systems, sentiment analysis in social media monitoring, automated translation, and text summarisation. It plays a vital role in enabling computers to understand and process human language, bridging the gap between human communication and machine understanding.

Key benefits of utilising NLP in finance

1. Increased efficiency – NLP programs convert large volumes of unstructured content into meaningful insights, in realtime.

2. Scaling – Using NLP technology, a voluminous amount of texts including a variety of documents, emails, social media data, and more can be analysed in seconds or minutes.

3. Process automation – NLP permits automation of the entire process of scanning and extracting actionable insights from financial data.

4. Accuracy – Human analysts at times oversee or misread content in large or voluminous unstructured documents (bias, inconsistency, or fatigue may play a role). NLP-backed systems, eliminate such errors to a greater extent.

5. Business risk reduction – NLP scans through the account history of customers, previous loan applications, and other data to provide the credit score and support the underwriting process.

6. Productivity – NLP extracts relevant information and provides summaries so that employees get better insight and focus on less repetitive tasks.

NLP applications in finance and banking sector

NLP is used in the finance and banking industry for a variety of purposes which includes improved decision-making, enrichment of data, automation, etc. It automates manual processes of turning unstructured data into a more usable form. Some of the applications are:

1. Financial documents analysis: NLP in combination with machine learning algorithms identifies and extracts significant data from financial statements, invoices, payment documentation, etc. The data is finally fed into an automation solution, such as a Robotic Process Automation bot for reporting financial reports. This helps financial professionals make informed investment decisions, identify market trends, and manage risks.

Use Case – Financial reporting – Deloitte’s Audit Command Language, an efficient NLP application uses NLP approaches to scrutinise contract documents and longterm procurement agreements, particularly with government data.

2. Investment research and analysis: NLP is employed to process vast amounts of financial research reports, analyst recommendations, and earnings call transcripts. It helps financial analysts extract key information, analyse trends, and make data-driven investment decisions.

Use Case – Stock price prediction – Companies like Bright Data’s Data Collector use NLP combined with machine language algorithms and web scraping/crawling tools to assess webbased financial news in real-time and facilitate news-based trading. Analysts use this information to isolate financial news that affects stock prices and market activity and send it to customers.

3. Credit analysis and loan underwriting: NLP is used to analyse loan applications, financial statements, and credit reports to assess the creditworthiness of customers and automate loan underwriting processes. It helps financial institutions streamline the evaluation process, reduce manual effort, and improve decision-making accuracy.

Use Case – Credit scoring – Companies like The LenddoScore use NLP to assist lender banks in credit scoring by extracting relevant data and pushing it to credit scoring software. These softwares also use NLP to extract information from the personal profiles of potential customers e.g., social media accounts, etc., and utilise ML algorithms to weigh these features and assess creditworthiness.

4. Document analysis and extraction: NLP techniques can extract relevant information from documents, such as resumes, invoices, receipts, or contracts. This aids in automating processes, improving efficiency, and reducing errors associated with manual data entry.

Use case – Resume evaluation – NLP in combination with KNN classification algorithms helps companies to screen candidates’ resumes, extract pertinent keywords (education, skills, previous roles), and categorise applicants based on their profile match to a certain position. It also helps to summarise the resumes of candidates who match specific roles. This helps recruiters scan through resumes faster and focus on specific requirements of the job. For example: – Amazon’s automated applicant evaluation system helps the company to identify candidates with the highest probability of success. It analyses resumes and compares them to profiles of other Amazon employees in similar roles and the best candidates are fast-tracked for interviews/promotions.

5. Compliance and risk assessment: NLP techniques help automate compliance processes by analysing legal and regulatory documents. It can identify relevant sections, extract key information, and ensure adherence to regulatory requirements. NLP also assists in risk assessment by analysing textual data to identify potential risks and fraudulent activities.

Use Case – Text analytics – This technique is used by Financial Institutions like the Bank of America and JP Morgan Chase for risk management. Data scientists, compliance managers, risk managers, etc. use it for scanning through financial documents to gain important insights.

6. Sentiment analysis: NLP is utilised to analyse customer sentiment expressed in social media, customer reviews, or feedback. Financial institutions can monitor customer sentiment towards their products or services, identify emerging trends, and take necessary actions accordingly.

Use Case – Understanding Customer Attitudes – Bank of Italy is using an AI project to understand customer sentiments from Twitter feeds and they also extract the tweets of other European banks like BMPS (Banca Monte dei Paschi di Siena), UCG (UniCredit), ISP (Intesa Sanpaolo), and Deutsche Bank, to understand how customers react to these banks. Later these reactions were used to predict customer preferences.

7. Customer support and chatbots – NLP – powered chatbots and virtual assistants are employed to provide personalised customer support, answer queries, and assist with various financial transactions. These bots can understand natural language inputs and engage in human-like conversations, improving customer satisfaction and reducing response times.

Also Read | Big Data Analytics: Improving Efficacy of Banking Services

Use Case – Omnichannel customer Engagement – A number of Banks use customer engagement tools like chatbots. These banks later use NLP to examine recorded conversations that might help them to improve their services.

8. Portfolio management and risk modelling: NLP techniques assist in analysing company filings, earnings reports, and news articles to evaluate investment opportunities and build risk models. It helps portfolio managers make informed decisions and manage risks effectively.

Use Case – Market assessment – IBM Watson’s Alchemy language tool assists banks to study market trends and interactions about their services and support the management process.

9. Transaction monitoring and fraud detection: NLP can analyse vast amounts of transactional data and account activities to identify patterns of transactions based on transaction type, amount threshold, channel, etc of potentially fraudulent activities, generate alerts and initiation of preventive/defensive mechanisms for reducing losses for customers/ institutions.

Use Case – Alert generation – State Bank of India has implemented a Proactive Risk Management (PRM) solution using AI & ML to detect and alert fraud/suspicious account/transaction activity like phishing, credit card fraud, internet banking fraud, and mobile banking fraud.

10. Language translation and localisation:

NLP facilitates language translation and localisation of financial documents, reports, or customer communications across different languages. This is particularly useful for multinational financial institutions operating in diverse regions. For example: – Microsoft Translator may be used to translate extracted local language data into the desired language for audit purposes.

The bottom line
The future of NLP in banking and financial institutions is promising. Financial institutions that embrace NLP technologies will gain a competitive edge, better serve their customers, improve risk management practices, and drive innovation. By using the power of NLP, banks can navigate the complexities of the digital age, stay ahead of industry trends, and create a more seamless and personalised banking experience for their customers.

Views expressed by Thomas Mathew, Research Officer, State Bank of India

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