Are you ready to entrust shoi your finances? Banks are put into work. What are the advantages and risks?

Artificial intelligence is no longer just a tool for banks, but the foundation of new financial reality. From automation of routine processes to personalized tips for Customers – AI converts each stage of banking operations. At the same time, the new chances are increasing threats that can be hit not only by business processes but also by customer trust. How to combine innovation with safety? CIO SENSE Bank tells Alexander Dragin.

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Ten years ago, Ukrainian banks were experimented with automation on the basis of simple IF-Ten-Rights. Currently, artificial intelligence builds a new banking architecture – digital assistants and generative models change the formats of client service and decision making.

The risks are increasing with the new horizons. Banks have to look for a balance between bold innovations and weighted liability. AI remains a strategic challenge for the industry.

From automation to Enterprise Ai The banking industry has traditionally applied intellectual automation (IA) to accelerateKnow Your Customer (know your client) is the process of identifying and verifying a client’s person conducted by financial institutions, exchanges and other companies to prevent money laundering, terrorism, fraud and other illegal actions.

“Data-Title> kyc,Anti-Money Laundering-Counteraction to money laundering.

“Data-Title> AMLcredit scoring. The transition to Enterprise AI is currently a combination of machine learning, generative models,Applied programming interface.

“DATA-TITLE> API and orchestration of processes. It is not about auxiliary tools, but about the core of the new operating model of the bank, which gives flexibility, scalability and rapid introduction of innovation.

This connection allows you to completely change the logic of the client service: from personal investment advice to dynamic risk management and autonomous processing of documents, according to SS & C Blue Prism.

Obvious advantages AI integration helps banks reduce costs by automating routine and reorienting human resources to strategic directions. AI models also reduce human errors in analyzing data and credit decisions.

Technology improves the quality of service and client experience: Chatbots 24/7, intellectual recommendations, seamless omnial services. The risk management of AI is also effective-from Fraud analysis to assessment of solvency in real time.

AI enhances compliance: systems with high accuracy provide monitoring and audit of actions in accordance with regulations.

Not everything is so simple: risks and challenges Every new opportunity also brings new threats.

BLACK-BOX-Effect: Most SI models (especially LLM) almost do not explain the logic of the conclusions obtained. Data Bias means that training samples are reproduced and scaled.

Hallucinations occur not only in people but also in generative models that are unacceptable in a highly regulated banking environment. Technical concentration enhances vulnerability-many banking wres rely on several providers, forming systemic refusals.

The increase in the complexity of models opens more vectors of attacks on data and algorithms, increasing cyberris.

A separate question is “model herd thinking”: the use of such models by many players increases the likelihood of synchronous errors and market volatility.

Trends by 2027 Four major trajectories of AI development in banking by analytics IIF, Forrester, Bank of England.

Human -centric shi. AI does not displace, but strengthens the employee. New roles in banks will be based on partnership with algorithms, not on purely control over them.

Agent models. Autonomous Ai Agents will gradually get some of the operational solutions. But key – saving the modelHuman-in-the-loop is a man-in-cycle. The principle of projection and operating practice of inclusion of human judgment in the decision -making processes of artificial intelligence systems.

“Data-Title> Hitl in critical areas.

Strict AI government. Frameworks such as Enterprise Operation Model (EOM) will be the standard for control, ethical application and regulatory reporting.

Conctor and ecosystem. AI should be built into the bank’s digital ecosystem through API, Orchestration and a single platform strategy.

A strategy is required, not a blind introduction The use of artificial intelligence in banking is rapidly becoming must-have. The question is not to “implement the si”, but how to do it responsibly and ethically.

There are cases when technologies are introduced for a “tick”, without taking into account a business model, culture of organization or ethical context. This creates reputational, operating and regulatory risks.

The strategy of the responsible AI should be embedded in the digital transformation of the bank as a system approach, not a separate technical project. It covers four key components:

Risk-management by design. AI models should be accelerated with risks rather than “extinguished fires” of post -factum. This includes the quality control and origin of training data, audit and validation of the results, monitoring of “hallucinations”, as well as a mandatory mechanism of human intervention at critical stages (HILT).

Ethical principles in the code, not only on slides. It is not enough to declare ethics in public documents. The norms should be laid down in the architecture of products, models of models, rules of moderation of input and issuance of solutions.

It is not only about discrimination, but also a deeper requirement: transparency of algorithms, responsibility for the consequences and ability to explain each automated solution.

Inclusive automation. Automation should not displace people. On the contrary, it should increase their efficiency by freeing time for tasks added. It means rethinking Rights and competencies in the bank, investment in training and adaptation of staff, creation of conditions for human partnership and AI, not competition.

Transparency of models and processes. The more complex the algorithm, the more questions it raises. Therefore, banks should be able to explain to the client the reasons for approval or refusal, provide the auditor with a logic of the solution, track the sources of data and the correctness of their processing, transparently report to the regulator on the use of algorithmic systems. Without this, there is a risk of losing the main thing – trust.

The banking industry is waiting for many decisions about the CI – both technological and worldviews. It is important to focus not so much on the choice of algorithms, but on what the future we form with them.

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