What is Knowledge Engineering?
Knowledge engineering is a field of study that is responsible for all technical, societal, and scientific aspects involved in the construction of artificial intelligence (AI).
Individuals working in the field of knowledge engineering are called “knowledge engineers.” They are in charge of building computer systems that are able to execute cognitive tasks and human decision-making.
- Knowledge engineering is a field of study that is responsible for the construction of computer systems that can make human-like decisions.
- Similarly, knowledge engineering in finance is the development of AI that can assist in financial decision-making, troubleshooting, analytics, and risk assessment.
- The main benefits of implementing a knowledge system are the increase in efficiency and the quality of information presented.
What is Knowledge Engineering in Finance?
Knowledge engineering in finance is a field of AI that uses data to create rules that emulate the thought process of a financial expert. With the use of a vast electronic library, AI can identify the task at hand and choose the most logical outcome.
The AI system contains a plethora of collateral knowledge and problem-solving techniques. The knowledge system’s main tasks include troubleshooting, diagnosis, and problem-solving, and it acts as a support system to a human agent.
Benefits and Issues of Financial AI
In addition to benefits, issues also arise when the financial AI is unable to make certain decisions as humans can. Shown below are the benefits and issues relating to financial AI:
|Faster decision making||Unable to get a “gut feeling” with regards to decision-making|
|Efficient problem-solving and troubleshooting||Cost of implementation|
|Available 24/7||Constant updating and refining|
|Reduction in human error||Lack of human judgment|
|Acts as a support system for a human agent||Cannot draw back to every experience|
|Unfavorable results are traced back and debugged||Continuous trial and error|
|Creation of financial knowledge engineering jobs||Potential reduction in human employment|
What is Financial Engineering?
Financial engineering is the use of mathematical procedures to solve financial roadblocks. It is a form of quantitative analysis that is used by large institutions, such as commercial banks, insurance agencies, and investment banks.
Financial engineering uses the knowledge to solve financial problems from a wide array of studies such as mathematics, statistics, economics, business, and computer science.
What is a Financial Engineer?
A financial engineer is a person who uses mathematical techniques to address problems in finance. He/She is also responsible for analytics and the implementation and creation of new investment tools.
Artificial Intelligence in Finance
The following are some of the ways AI is being used in finance today:
- DataRobot – It is a software company that helps financial institutions and businesses construct predictive models that enhance certain decision-making scenarios such as fraudulent credit card transactions and lending.
- Scienaptic Systems – It offers an underwriting platform for credit institutions and banks that provides increased transparency and reduced losses.
- Kensho – It offers data analytics, analytical solutions, and machine intelligence to some of the largest companies in the world, such as J.P. Morgan and Bank of America.
- Alphasense – It is an AI-powered search engine used by financial institutions. Its search engine allows users to observe changes and trends in the financial market, along with in-depth research and transcripts.
- Kavout – It allows its users to view real-time trends in the financial market. In addition, Kavout offers a “Kai Score,” which is an AI-powered stock ranker that allows consumers to view a list of stocks with the highest amount of potential.
- Ayasdi – It is an AI company that provides cloud-based intelligence solutions for problems such as money laundering and risk management.
Financial Positions that Require AI Knowledge
As time passes, businesses are becoming more involved with knowledge systems and artificial intelligence. Listed below are financial occupations that require AI knowledge:
1. Senior Product Manager of Commercial Credit (Capital One)
Applicants are required to possess knowledge in AI, predictive analytics, automation, and machine learning to assess potential risk (approximate salary = $65,000-$115,000).\
2. Senior Experience Designer (Bank of America)
Applicants must be knowledgeable in digital design and AI to create user-centered experiences for their digital platforms (approximate salary = $112,000-$123,000).
3. AI Backend Engineer (J.P. Morgan)
Applicants must possess knowledge in AI and be responsible for creating a back-end AI system that can process data and provide machine learning requests (approximate salary = $90,000 – $110,000).
Thank you for reading CFI’s guide to Knowledge Engineering. To keep advancing your career, the additional CFI resources below will be useful: