AI Strategies for Fintech Firms: Insights from Data Scientist 

In the rapidly evolving fintech landscape, harnessing the power of natural language processing (NLP) models is essential for firms with access to vast amounts of text data, as highlighted in Fintech Nexus. According to Sumedha Rai, a senior data scientist, fintech firms that neglect NLP are missing significant opportunities.

Rai emphasizes that NLP, a branch of artificial intelligence that teaches machines to understand, analyze, and generate human language, should be a cornerstone of a fintech firm’s strategy. By employing NLP models, companies can analyze internal and external text data to understand customer and employee sentiments, identify key business trends, and enhance their overall strategy.

The emergence of generative AI has further amplified the capabilities of NLP. Speaking at the AI in Finance Summit and MLConf 2024 in New York City, Rai highlighted that NLP tools, combined with other machine learning and AI solutions, can rapidly summarize and translate documents, detect fraud by identifying anomalies in communications, and personalize customer interactions.

Rai’s expertise stems from her role at a micro-investment firm in New York City, where she analyzes user sentiment, assists in investment decisions, and develops fraud prevention models. She also collaborates with New York University’s Center for Data Science.

She notes that the most significant benefit of regular text analysis via NLP is efficiency, which allows employees more time for creative tasks related to product development and business strategy. This, she asserts, offers a distinct competitive advantage.

For fintech firms, NLP analysis is valuable for assessing customer feedback, social media comments, transaction data, employee communications, regulatory and compliance data, and more. Rai advocates for quarterly or ongoing assessments to customize services, build better chatbots, detect fraud, and understand employee satisfaction.

One specific NLP technique, topic modeling, can track customers’ preferences and dislikes. Rai suggests fintech firms consider how much signal they have received on their challenges and leverage NLP to address these issues.

Rai recommends several NLP models, including Latent Semantic Analysis (LSA), Latent Dirichlet Allocation (LDA), LDA2vec, and BERTopic. For financial text, she favors FinBERT, a transformer model pretrained on financial data, particularly the bi-directional BERT models for their contextual understanding.

Another underutilized NLP technology is Named Entity Recognition (NER), which tags text to categorize entities. NER can help tailor customer communications, extract critical information from large texts, and flag potential fraud. Rai notes that NER can quickly extract key information from compliance documents, streamlining the review process.

With generative AI models, fintech firms have powerful tools for text analysis that require minimal coding. Rai appreciates the ease of use of Chat GPT and Meta’s LLAMA models. However, she cautions against feeding sensitive data to these models due to data security risks. She advises stripping personally identifiable information (PII) from data before using it with generative AI models.

Rai also stresses the importance of evaluating models for bias, discrimination, data security, and privacy. She recommends employing diverse teams to work on models and using external red teams to ensure models are unbiased and non-discriminatory.

One practical application Rai highlights is using Chat GPT for creating logos, taglines, and press releases, noting the impressive results and ongoing improvements in generative AI capabilities.

For fintech firms looking to stay ahead, integrating NLP and generative AI into their strategy is not just an option but a necessity.