Previously, I analyzed how dirty, or bad data is the enemy of machine learning. The final blog of this series describes the advantages of artificial intelligence and machine learning in financial services.
Many financial services organizations have already begun to take advantage of ML technology because of its proven ability to reduce operational costs, increase revenues, improve productivity, enhance compliance, bolster security, and enrich the customer experience. However, most companies are in the early stages of exploiting the benefits of ML.
Digital transformation challenges in banking have been well understood and the strategies to address them simple and clear. However, it is becoming increasingly apparent that the industry is reaching a tipping point in the digital transformation journey.
With more than 10,000 AI vendors competing for market share, there exists a range of ML applications for different use cases in financial services. Some applications, such as chatbots, are industry-agnostic and need to be trained for the specific company’s vernacular. Others are highly specific to financial processes. If a firm has the impetus, sufficient technology talent, and financial resources, there are open-source ML frameworks that can be leveraged to facilitate custom software development.
If properly implemented, AI can provide financial services firms with a superior customer experience (CX) and increase revenue, reduce expenses, boost security, and mitigate fraud.
Given the complexities of ML algorithm selection, data science, and model training, validation, and testing, having a trusted guide, such as Perficient, can help avoid missteps and ensure success. With dedicated practice areas in AI, data management, and financial services, as well as partnerships with leading AI/ML vendors, Perficient is uniquely positioned to assist with your data management strategy and the selection and deployment of AI/ML tools throughout your enterprise. We have helped numerous financial services firms assess their data quality, identify incomplete or inconsistent datasets for remediation, and implement process and technology improvements to ensure proper edits, validations, and controls going forward.
To learn more about the specific differences between AI and ML, dirty data, and ways to take advantage of these technologies you can click here or fill out the form below.