Credit Authorizers, Checkers, and Clerks

Banks and lenders increasingly use software and AI to automate parts of credit clerks’ work. For example, algorithms now compile and score credit histories much faster than people could. A recent study notes that AI “synthesizes large amounts of data” and speeds up credit rating tasks like combining a borrower’s financial info into a score ^[1].

Digital records systems and RPA (robotic process automation) handle routine bookkeeping (tracking charges and payments) automatically. Even customer interviews are partly automated: some simple loan questions are now handled by chatbots or automated phone systems instead of people ^{[2] [3]}. AI tools can also draft summary reports – for instance, large language models can analyze data and write up findings in full sentences ^{[4] [3]}.

However, these technologies generally handle routine parts of the job. Human workers are still needed to ask nuanced questions, explain decisions, and understand complicated situations.

AI is catching on in finance because of clear cost and speed benefits. Big banks say AI can cut processing time and errors (JPMorgan’s COiN system famously saved hundreds of thousands of work-hours). Cutting labor costs is attractive, so many firms are experimenting with AI for credit and lending tasks ^{[2] [1]}.

But adoption in this field is cautious. Credit decisions are highly regulated and affect people’s lives, so banks worry about fairness, data quality and explainability before trusting AI blindly ^{[4] [1]}. In practice, many lenders mix AI tools with human oversight.

Young workers can take heart that jobs involving judgment, relationship-building, and ethical reasoning – not just data entry – remain important. The available evidence suggests some tasks will shift to automated systems, but human skills like trust and complex problem-solving are still valuable ^{[2] [3]}.