Artificial Intelligence Fuels the Future of Finance

credit:  LTP

credit: LTP

Artificial Intelligence (AI) is the science of applying computer systems to perform tasks that require human intelligence — such as seeing, speaking, recognizing speech, and making decisions.  AI was first conceived in the mid-20th century, with computer programmers focused on decision-mapping models that lead to predictive user activity. Due to the availability of big data, cheap storage, high-speed computing, and increased connectivity — AI has risen to new heights in the last decade. It's available today in our voice-enabled devices and smartphone Q&A recommendations, working from large volumes of data and analytics.

Capabilities and Competencies

AI develops its capabilities through both supervised and unsupervised learning. Supervised learning involves data scientists training systems with data and guidance in making decisions (e.g. differentiating an image of windows versus one of mirrors reflecting windows). Unsupervised learning works large volumes of random data in order to view associations and patterns that lead to new insights.

There are eight core competencies in artificial intelligence that can be broken down into two subsets: absorbing information, or explaining information.

In the first subset, speech & image recognition, clustering, and search competencies capture unstructured information and convert it to structured data.

The second subset has natural language understanding (NLU), prediction, optimization, and comprehension. These activities require some form of cognition either by transforming data, or solving problems through reasoning and algorithms. Comprehension involves AI systems becoming self-aware of activities and decision-making.

Artificial intelligence is being used on its own or with other technologies such as cloud computing, robotic processing automation (RPA), or Internet of Things (IoT). The evolution of AI (combined with these other technologies) has led to enhancements in customer service (through chatbots), customized recommendations for purchases, and improvements in supply chain.

In the banking and finance industry, AI improves customer journey optimization, fraud reduction, money laundering detection, and targeted advertising.

Challenges and Outlook

The risk with AI today is the need for a vast volume of quality data that requires a huge effort in collecting, organizing, and analyzing — all in an organized manner without bias.  

AI needs to integrate with core processes in a business to be truly beneficial with its recommended insights.  This integration requires a heavy concentration of security controls protecting data.  

Timing will be critical to on-boarding this new technology, specifically in making sure it fits well with people and culture at companies.  Inaccuracies in AI models will also need to be cleansed with human oversight to address lack of understanding from insights.

Business leaders interested in implementing artificial intelligence should focus on challenges impacting their company, and the ways AI can create solutions to resolve these needs.

FinTechtris articles on AI:

Machine Learning in the World of Finance

AI, the Next Level for FinTech

Machine Learning for Today’s Business World

Be Ready for AI Impacting You at the Office

FinTech using AI and Machine Learning to Reach Millennials

2018: (the Breakout) Year of Artificial Intelligence