Conversational AI Agents for Sales Forecasting in Financial Enterprises

Srikanth Balla; Sanjay Kumar Bahl

doi:10.36676/jrps.v14.i5.1660

Authors

Srikanth Balla Christian Brothers University Memphis, TN, USA
Sanjay Kumar Bahl Indus Intenational University Haroli, Una, Himachal Pradesh – 174301, India

DOI:

https://doi.org/10.36676/jrps.v14.i5.1660

Keywords:

Conversational AI, Sales Forecasting, Financial Enterprises, Predictive Analytics, Natural Language Processing, Forecast Accuracy, AI-Driven Decision Support, Human-Centric Interfaces, Intelligent Agents, Real-Time Insights.

Abstract

Sales forecasting is an integral part of financial institution strategic planning that has implications for budgeting, inventories, and resource allocation. Traditional forecasting methods like time-series analysis and statistical modeling might not be able to encapsulate the nuances of modern market trends and changing consumer behavior. The advent of artificial intelligence and machine learning has opened up new avenues for designing forecasting models; however, the models tend to be technical in nature and lack intuitive interfaces for non-technical end-users. This research realizes a pertinent gap in the literature—the less-highlighted role of conversational AI agents in providing interactive, user-friendly, and advanced sales forecasting platforms in financial institutions.

References

• Araci, D. (2019). FinBERT: Financial sentiment analysis with pre-trained language models. arXiv preprint arXiv:1908.10063. https://arxiv.org/abs/1908.10063

• Benthall, S., & Haynes, B. (2019). Rethinking the ethical challenges of AI in finance. Journal of Financial Regulation and Compliance, 27(3), 286–302. https://doi.org/10.1108/JFRC-05-2019-0050

• Fails, J. A., & Olsen, D. R. (2015). Interactive machine learning. Proceedings of the 8th International Conference on Intelligent User Interfaces (IUI ’15), 39–45. https://doi.org/10.1145/2678025.2701399

• Gatt, A., & Krahmer, E. (2018). Survey of the state of the art in natural language generation: Core tasks, applications and evaluation. Journal of Artificial Intelligence Research, 61, 65–170. https://doi.org/10.1613/jair.5477 DOI: https://doi.org/10.1613/jair.5477

• Guidotti, R., Monreale, A., Ruggieri, S., Turini, F., Giannotti, F., & Pedreschi, D. (2018). A survey of methods for explaining black box models. ACM Computing Surveys, 51(5), 93. https://doi.org/10.1145/3236009 DOI: https://doi.org/10.1145/3236009

• Kim, J., & Park, S. (2020). Cloud-native AI architectures for scalable forecasting systems. Journal of Cloud Computing, 9(1), 14. https://doi.org/10.1186/s13677-020-00182-3

• Kulesza, T., Burnett, M., Wong, W.-K., & Stumpf, S. (2015). Principles of explanatory debugging to personalize interactive machine learning. Proceedings of the 20th International Conference on Intelligent User Interfaces (IUI ’15), 126–137. https://doi.org/10.1145/2678025.2701399 DOI: https://doi.org/10.1145/2678025.2701399

• Li, X., Su, H., Shen, X., Li, W., & Li, S. (2017). Deep reinforcement learning for dialogue generation. arXiv preprint arXiv:1709.05413. https://arxiv.org/abs/1709.05413

• Meduri, A., Danilevsky, M., Oztok, U., Kulkarni, C., & Parameswaran, A. G. (2021). BI-REC: An interactive conversational recommendation system for business intelligence. Proceedings of the 2021 ACM SIGMOD International Conference on Management of Data, 2549–2554. https://doi.org/10.1145/3448016.3452835 DOI: https://doi.org/10.1145/3448016.3452835

• Singh, A., Kaur, P., & Kaur, R. (2019). Multi-modal deep learning framework for sales prediction combining numerical and textual data. International Journal of Computer Applications, 178(20), 23–29. https://doi.org/10.5120/ijca2019919046 DOI: https://doi.org/10.5120/ijca2019919046

• Zhou, Z., Yu, X., Chen, J., & Li, B. (2020). AI-powered chatbots for financial customer service: Evaluation and implementation. International Journal of Bank Marketing, 38(5), 1100–1119. https://doi.org/10.1108/IJBM-05-2019-0194