2019 will be an exciting year in the field of data science for the financial industry. There will be three key trends that are poised to speed up the pace of machine learning and artificial intelligence (AI) adoption.
- Operationalizing the end-to-end big data pipeline
- Cross-industry partnerships for data-sharing
- Open dialogue on regulations and information security policies
Operationalizing the End-to-end Big Data Pipeline and Machine Learning Engineers
May financial institutions will start operationalizing their data science pipelines, which they had spent the past year laying the architectural foundation for. There will be a need to kick-start the deployment of data science models and big data to train these models. As we move from ideation to implementation, this will significantly impact how the financial industry applies data science and AI to the business.
Operationalizing the modern data stack will bring about a restructuring of people, platforms and processes to harness data and accelerate the applications of machine learning and AI. There will be a heightened focus on machine learning engineering roles in the maintenance and enhancement of an in-production data pipeline to manage big data and computational resources.
At the same time, project management will undoubtedly shift towards agile methodologies, given the continuous deployment of software components to enhance the data stack.
Cross-industry Partnerships for Data-sharing
We are at the cusp of a growing trend towards cross-industry data-sharing partnerships. In the Southeast Asia region, banks are partnering e-commerce and telecommunication firms to leverage on data to support customer products and services.
Cross-industry data sharing will lead to significant improvements in customer background checks and credit underwriting, which will serve a wider customer segment. This trend does not only apply to business-to-business partnerships, but also to governments-to-industry engagements. More governments in the region are strengthening government-verified personal data services for social benefit.
Singapore has been at the forefront of this effort, allowing banks to use government data services, via open Application Programme Interface (APIs). A benefit of this initiative is that customers, who seek loan financing, no longer have to prepare and submit thick stacks of documentation. Many countries in the region will soon follow in this direction.
Open Dialogue on Regulations and Information Security Policies
While AI technology is ready for adoption, regulations and information security policies will need to keep pace with the ever-changing technology landscape. Governments recognize the need to provide the right regulatory environment for banks to operate and stay competitive while protecting their citizens’ personal data privacy. At the same time, organizations understand the need to review their information security policies to find the balance between implementing AI-backed initiatives and putting in place necessary safeguards for customer data security.
In the year ahead, there will be an increase in open dialogue on a range of machine learning and AI topics that concern citizens and industry players. This stems from the urgency to dedicate focus and attention to tackling the consequences of machine learning and AI, so that we can reap the benefits from AI adoption, while preventing the abuse of data.
This article was first published on Learn@IBF.
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