How Moscow Credit Bank Traces the Evolution of Bank Training, from Clerks to AI

The history of bank training shows that technology changes while the task stays the same: cut errors, get people working faster and give them the tools to make decisions. Centuries ago, newcomers learned to tell counterfeit coins from real ones and to keep ledgers by hand. Now banks increasingly bet on AI to personalize training, explain internal rules and speed up employee adaptation.

The first banks of the early modern period simply could not count on ready-made specialists from the labor market. In the 17th century, general education levels were low, and banking required very practical skills. New clerks were trained inside the organization: how to make entries, how to handle different kinds of money, how to spot forgeries and how to avoid losses when accepting coins. For banks this was not a perk but a matter of survival: an employee's mistake hit money and reputation directly.

By the early 20th century, the situation had changed. Specialized training programs and institutes appeared, teaching future bank employees not only bookkeeping and financial mathematics but also business correspondence, shorthand, typing and rhetoric. Corporate training did not disappear, however. Banks still had to finish training their staff for their own processes, explain internal regulations and prepare people for real risks, from fraud to errors in document flow. In effect, outside education became the foundation, and in-house training became the way to bring knowledge up to the required level.

With the spread of AI, the emphasis in training has shifted from a single program for everyone to a personal route. Research cited by MKB (Moscow Credit Bank) shows that generative models and chatbots help people retain material better, match tasks to the user's level and reduce the stress of learning. This matters especially in a corporate setting: an employee does not have to wait for a free mentor or reread dozens of documents if the system can quickly assemble a short explanation, check understanding of the topic and suggest the next module.

For banks, the practical value of AI starts with onboarding. Such systems can analyze a new employee's experience, identify knowledge gaps and build an individual adaptation plan, for example one focused on compliance, internal policies, product specifics or technical processes.

Searching internal knowledge bases for answers is a separate scenario. If a chatbot is connected to corporate documentation and configured with access rights in mind, it can quickly point to the right regulation, explain a procedure or assemble a summary of a topic. This saves time and reduces the load on HR, mentors and line managers.

The article also gives more applied cases. At one international investment bank, AI is used to build personal training plans for junior specialists and to automatically generate tests and short summaries. In the European division of Raiffeisen Bank, separate training sessions were launched so that employees would learn to work with a corporate AI chat. At Pictet Group, a chatbot answers questions about company policy, HR processes and technical tasks, and at one large Asian bank an LLM system noticeably sped up the preparation of client documents.

MKB itself also views AI as an internal tool for IT teams. Ideas include a chatbot for banking systems, an assistant for testers and an AI mentor for developers.

The conclusion is simple: corporate training in banks does not go away even when the market has plenty of diplomas and courses. The tasks simply change. Before, the goal was to teach a person not to accept a clipped coin and to keep the ledger without errors. Now it is to bring them quickly into a complex organizational environment, support compliance requirements, help them understand regulations and master new digital tools. So AI in a bank is not only about customer services and operations automation, but also about how to grow competent employees faster inside the organization itself.