For businesses looking to streamline their lending processes and create more efficient funding opportunities, automation may be the key. Automation can help reduce bias from entering into decisions when it comes to approving loans or providing access to financing – making the process fairer for all applicants. In this blog post, Matt Nicosia discusses how automation is helping businesses reduce bias in the financial services industry – and find new ways of ensuring that everyone has an equal chance at success.
Matt Nicosia On How Automation Can Streamline And Reduce Bias In The Funding Process
Automation can be an invaluable tool for streamlining and reducing bias in the funding process, says Matt Nicosia. Automated systems, such as machine learning algorithms, can help to remove assumptions and preconceived notions from a decision-making process. Automation reduces manual error, helps to maintain data accuracy, ensures transparency and fairness within the system, and promotes consistency in decisions made.
In addition to reducing human error, automation also helps to reduce systemic bias by eliminating individual preferences or opinions from the process. Automated decisions are based on consistent guidelines that do not vary across different people or businesses seeking funds. This increases trust in the system because all participants know their chances of success will be evaluated equitably regardless of who is making the decision.
Data-driven decisions also reduce cognitive bias, as decision-makers are no longer subjectively weighing the merits of any given criteria. Automated systems allow for objective assessments to be made based on collective data. Automation is also becoming increasingly popular in customer service and loan underwriting, helping banks and other financial institutions make better lending decisions with fewer biases from individual bankers.
According to Matt Nicosia, the use of automation to streamline and reduce bias in the funding process has seen positive results across various industries. According to a study conducted by US Bank, using Automated Underwriting System (AUS) technology led to an 84% increase in loan origination efficiency while reducing bias-related errors by over 60%. Automation has also seen great success when used within job recruitment processes. Automated software is now able to scan resumes for relevant keywords and quantify key skills and work experience; this eliminates any bias that may arise from the recruitment process by reducing manual errors and promoting fairness in the selection decision.
One example of the success of automation in the funding process can be seen with Automata Ventures, a VC firm focused on artificial intelligence investments. Automata has created an automated system known as Signals which leverages machine learning algorithms to sift through thousands of potential deals. This system helps Automata make swift decisions while ensuring no bias exists in their selections; one investor at Automata noted that “Signals takes out a lot of the human biases – like who your uncle or aunt knows” when making investment decisions. Automata also noted that Signals has led to higher success rates in investments, with their portfolio companies seeing a 20-30% increase in revenue year over year since the system was implemented.
Matt Nicosia’s Concluding Thoughts
In summary, Automation can be an invaluable tool for streamlining and reducing bias in the funding process. According to Matt Nicosia, automated systems help to remove human error and subjectivity from decision-making, promote data accuracy and fairness within the system, as well as consistency across different applicants or investors seeking funds. Automation also helps to reduce systemic biases by ensuring all participants are evaluated equitably regardless of who is making the decision.