Natural Language Processing in banking

Let's face it, the volume of textual information generated daily by financial institutions is overwhelming. From sector reports and news to customer feedback on social media, and everyday banking transactions, the abundance of unstructured data presents both an opportunity and a challenge.

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Fortunately, Natural Language Processing (NLP) emerges as a solution allowing banks and other financial entities to harness the power of information.

As the adoption of NLP accelerates in the financial sector, we are witnessing a revolution in risk management, operational improvement, and customer experience optimization. In this article, I’ll tell you how Natural Language Processing is redefining the banking and finance landscape, offering institutions an unprecedented competitive advantage.

Óscar Barba

Co-founder & CTO of Coinscrap Finance

What is Natural Language Processing?

It’s a branch of artificial intelligence that focuses on interaction between computers and human language. Natural Language Processing enables machines to understand, interpret, and generate text similar to humans. Applying computational linguistics techniques, this technology transforms natural language into a format computers can understand.

In the financial context, NLP plays a crucial role by enabling institutions to extract valuable information from a wide range of unstructured data sources. NLP also facilitates understanding and processing of this flood of textual information. Now let’s see its role in AI, how it’s used in the financial sector, and what the future holds for this technology.

What does NLP mean in the context of AI?

Natural Language Processing uses rule-based approaches or machine learning to understand the structure and meaning of text. Research in this field has ushered in the era of generative artificial intelligence, covering advanced communication capabilities of large language models (LLM) to image generation tools interpreting requests.

Its algorithms are already part of everyday life for many, powering search engines, voice-operated GPS systems, and digital assistants on smartphones, among other applications. Its journey spans over 70 years, starting as technology applied in linguistics and statistics, and now seemingly limitless!

Key applications of Natural Language Processing in the banking sector

Sentiment analysis and anomaly detection

By processing and analyzing language used in customer feedback, social media posts, and other channels, NLP enables financial institutions to understand customer perceptions and satisfaction levels.

This not only helps identify areas for improvement but also facilitates early detection of potential issues; it’s used to pinpoint anomalies in textual data like suspicious patterns in transactions or irregularities in communication.

This understanding of natural language allows institution teams to analyze large volumes of information automatically and respond promptly to prevent potential losses.

Automated reporting and content generation

Traditionally, risk and compliance reporting has been a labor-intensive and error-prone process. However, NLP has transformed this task by automating the extraction of relevant information, summarization, and report generation.

Time and resource savings, along with result precision and consistency, represent a paradigm shift. This technology is also used to efficiently generate content, from customer communications to marketing and outreach materials.

From drafting communications with customers, to creating marketing and outreach materials, NLP is helping financial institutions optimize their content generation processes.

Automated document classification

Handling and examining content from reports, contracts, requests, and other documentation is now a matter of seconds for Natural Language Processing. Its ability to tag and organize information efficiently facilitates retrieval and enables subsequent ad hoc analysis.

This capability of automated classification is particularly valuable in areas like risk management, regulatory compliance, and customer service, where extremely rapid information retrieval is crucial.

Enhancing customer experience

It’s one of the aspects that the banking world has been focusing on more lately. By feeding chatbots and virtual assistants with natural language processing capabilities, institutions can offer customers more personalized and real-time responses to their queries.

Furthermore, sentiment analysis, as mentioned earlier, allows financial institutions to better understand customer needs and preferences, helping them design hyper-personalized products and services.

Fraud detection and prevention

It’s a constant concern for any company, but banks are particularly vulnerable due to the sensitivity of the data they handle. NLP can analyze patterns and anomalies in large volumes of transactional data.

Its understanding of natural language allows it to detect suspicious activities, enabling financial institutions to respond quickly and appropriately, preventing losses. All this helps compliance teams address fraud attempts more effectively.

Data-driven decision making

Perhaps NLP’s greatest contribution to the banking sector is its ability to transform unstructured data into valuable information supporting decision making.

Natural Language Processing generates insights that allow financial entities to make more informed and strategic decisions. This ability to create intelligence from textual data is especially relevant in areas like strategic planning and designing products and services tailored to customers.

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Challenges and considerations in Natural Language Recognition

After considering all the benefits that Natural Language Recognition offers the banking sector, it’s time to understand the challenges that banks, neobanks, and tech companies must face when using their algorithms.

These are the aspects to keep in mind:

Privacy and data security

When processing large amounts of unstructured information, it’s essential for companies to implement data protection measures and comply with privacy regulations.

Proper management of confidential customer information and prevention of security breaches are paramount.

Accuracy and potential model bias

Regarding the quality and representativeness of the data used in training NLP models, ensuring accuracy and avoiding bias is crucial.

Companies must dedicate time and resources to carefully select and prepare datasets, as well as continuously evaluate model performance.

Transparency and explainability

As Natural Language Recognition systems become more sophisticated, the ability to explain the decisions and results generated by these models becomes crucial.

Financial institutions are required to prioritize transparency and provide clear explanations of how their NLP-based systems operate.

Integration with existing technology

Successful implementation of NLP requires seamless integration with existing systems and processes. This necessitates having an expert team on board.

Carefully addressing technical and organizational challenges will ensure a smooth transition and maximize the benefits of natural language analysis.

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The Future of Natural Language Processing in banking

As technology continues to advance, it’s evident that natural language recognition will play an increasingly important role in transforming the financial sector.

Increasingly sophisticated NLP models will provide companies with a deep understanding of diverse customer realities. Its ability to process and analyze data in real-time will drive highly specialized services.

Not to mention its focus on people’s needs throughout their life journey. This will be key to standing out from the competition.

That’s where 100% human interactions gain value, based on quality insights and relevant data history. Enhancing customer experience has never been easier.

Take a look at our project “Hyperpersonalised Financial Intelligence

About the Autor

Óscar Barba is co-founder and CTO of Coinscrap Finance. He is an expert Scrum Manager with more than 6 years of experience in the collection and semantic analysis of data in the financial sector, classification of bank transactions, deep learning applied to stock market sentiment analysis systems and the measurement of the carbon footprint associated with transactional data. 

With extensive experience in the banking and insurance sector, Óscar is finishing his PhD in Information Technology right now. He is an Engineer and Master in Computer Engineering from the University of Vigo and Master in Electronic Commerce from the University of Salamanca. In addition, Scrum Manager and Project Management Certificate from the CNTG, SOA Architecture and Web Services Certificate from the University of Salamanca. He recently obtained the ITIL Fundamentals certification, a recognition of good practices in IT service management.

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