AI financial sector

The financial industry is at the centre of the global economy and encompasses a wide range of services, including banking, insurance, investment and financial advice. This industry is characterised by strict regulations, high security requirements and intense competition. With digitalisation and rapid technological development, customer requirements and expectations have changed, making the implementation of artificial intelligence (AI) a necessity.

Five key challenges when implementing AI in the financial sector:

Data security and data protectionThe financial sector works with highly sensitive data. Protecting this data from cyberattacks and complying with strict data protection regulations are of the utmost importance.
Regulatory requirementsFinancial companies have to fulfil a large number of regulations and compliance requirements. The integration of AI must fulfil these requirements.
Cultural changeThe introduction of AI requires a cultural change within the company, as employees may have resistance to new technologies.
Integration into existing systemsFinancial institutions often have complex and outdated IT infrastructures that make it difficult to seamlessly integrate AI technologies.
Trust and transparencyCustomers and stakeholders must have confidence in the AI systems. The transparency of algorithms and decisions is therefore essential.

Why a company-wide AI strategy is necessary

A coherent and standardised AI strategy ensures that all departments within a company work in sync and pursue the same goals. This avoids silo thinking and enables resources to be utilised more efficiently. In addition, a company-wide strategy promotes data consistency and improves decision-making through centralised data analyses. A unified AI strategy also supports adherence to compliance and security standards and promotes a unified corporate culture that emphasises innovation and adaptability.

Why the KIROI strategy is so highly valued by over 400 companies

The KIROI masterplan offers a structured and practical approach to implementing AI in the financial sector. Through the 9 clearly defined steps, KIROI ensures that all relevant aspects - from knowledge transfer to skills development - are covered. KIROI emphasises the importance of ethics and compliance and promotes a culture of collaboration and continuous learning. This makes KIROI the ideal solution for financial organisations that want to implement AI successfully and sustainably.

KIROI masterplan for the implementation of AI in the financial sector

Step 1: Share your knowledge

MeaningKnowledge sharing is the first step towards the introduction of AI. Discussions with managers, IT teams and specialist departments promote a shared understanding of the potential and challenges of AI. Involving all relevant stakeholders creates a basis for acceptance and support of the AI initiative.

Identify internal experts and AI enthusiasts.
Organise regular knowledge exchange meetings.
Promote interactive workshops on AI topics.
Develop an internal communication strategy.
Create a knowledge database on AI applications.
Rely on transparent communication.
Involve external experts for additional perspectives.
Use internal platforms for the exchange of knowledge.
Create a network of AI ambassadors within the company.
Document and share success stories.

Step 2: Explore tools

MeaningUnderstanding and selecting suitable AI tools is crucial for successful implementation. It is important to identify the tools that best meet the specific needs and objectives of each department.

Analyse the current technology stack.
Identify suitable AI tools for different tasks.
Carry out pilot projects to test the tools.
Ensure that the tools are compatible with existing systems.
Consider the scalability of the tools.
Evaluate the user-friendliness and acceptance of the tools.
Create training resources for the new tools.
Carry out regular evaluations of the tools.
Take security and data protection aspects into account.
Develop a long-term technology roadmap.

Step 3: Big data and smart data

MeaningCollecting, processing and analysing large amounts of data is the backbone of any AI application. By utilising big data and smart data, financial companies can gain valuable insights and make informed decisions.

Identify relevant data sources within the company.
Develop a strategy for data collection and storage.
Implement robust data management systems.
Use data analyses to identify patterns and trends.
Promote collaboration between data scientists and specialist departments.
Implement data quality assurance measures.
Ensure that the data complies with the data protection guidelines.
Use advanced analysis tools for data processing.
Develop dashboards to visualise the data.
Create a culture of data-driven decision-making.

Step 4: Cultural topics

MeaningSuccessful AI implementation requires a positive corporate culture that supports innovation and change. Employees must accept change and be willing to continuously develop themselves further.

Promote an open and innovative corporate culture.
Offer training and further education programmes.
Communicate the benefits of AI clearly.
Create incentives for the use of AI tools.
Introduce regular feedback loops.
Support interdisciplinary collaboration.
Provide support and resources for change.
Recognise and reward AI engagement.
Promote a culture of error as a learning opportunity.
Integrate AI topics into the corporate culture.

Step 5: Ethics and compliance

MeaningCompliance with ethical standards and legal regulations is essential. Financial organisations must ensure that their AI applications are transparent, fair and responsible.

Develop an ethical framework for the use of AI.
Ensure that all AI applications are transparent.
Implement bias checking measures.
Create clear guidelines for data protection.
Carry out regular compliance audits.
Create an ethics committee for AI issues.
Create awareness of ethical challenges.
Develop training programmes on ethics and compliance.
Consider ethical aspects when developing new applications.
Communicate ethical guidelines clearly and regularly.

Step 6: Own department

MeaningEach department should develop specific ideas and applications for the use of AI in order to increase their efficiency and effectiveness.

Analyse the specific needs of the department.
Identify processes that can be optimised using AI.
Develop customised AI solutions.
Carry out pilot projects.
Make the successes visible.
Create training programmes for the department.
Promote the acceptance of new technologies.
Implement continuous improvement processes.
Ensure that the AI solutions are in line with the department's objectives.
Consider feedback from the department to optimise the AI applications.

Step 7: Other departments

MeaningCross-departmental collaboration in the implementation of AI promotes synergies and maximises the benefits for the entire company.

Share best practices between departments.
Promote the exchange of knowledge and cooperation.
Develop joint AI projects.
Ensure that the AI strategies are harmonised.
Utilise synergies to increase efficiency.
Hold regular cross-departmental meetings.
Recognise common challenges and develop solutions.
Implement centralised data management.
Support the networking of AI teams.
Consider cross-departmental feedback for optimisation.

Step 8: Expertise of employees

MeaningContinuous training of employees is crucial in order to fully utilise the benefits of AI.

Develop customised training programmes.
Encourage participation in external training programmes.
Create learning platforms for employees.
Offer regular workshops and seminars.
Support the exchange of knowledge among employees.
Create incentives for continuous learning.
Implement mentoring programmes.
Use e-learning platforms.
Encourage the internal exchange of learning resources.
Take into account the individual learning needs of employees.

Step 9: Competence of managers

MeaningThe development of leadership skills is crucial for the successful implementation of AI strategies. Managers must be able to lead and promote change.

Develop special training programmes for managers.
Encourage participation in leadership workshops.
Create mentoring programmes for managers.
Support the exchange of knowledge among managers.
Promote a culture of continuous learning.
Create incentives for the further development of leadership skills.
Develop programmes to promote change management.
Support participation in external leadership programmes.
Promote the use of AI tools for decision support.
Consider the individual development needs of managers.

The view from scientific research

The potential of AI in the financial sector

AI technologies such as machine learning and natural language processing enable financial institutions to analyse huge amounts of data in real time, recognise patterns and make predictions. This makes it possible, for example, to better assess credit risks, detect fraud attempts at an early stage and offer personalised customer services[1][3]. According to a study by Accenture, the use of AI can increase productivity in the financial sector by up to 30%[11].

Technical challenges

However, the implementation of AI systems poses considerable technical challenges for financial institutions. A key problem is the quality and availability of training data[2]. Especially in newly founded or rapidly growing companies, there is often a lack of historical data sets. In addition, financial data is often distributed across different systems, which makes integration and processing more difficult[1].

The selection of suitable AI models and algorithms is also complex. Overly complex models tend to "overfit", i.e. they deliver poorer results on test data than in the training phase[2]. A great deal of experience and fine-tuning is required here.

Ethical and regulatory aspects

In addition to the technical hurdles, the use of AI in the financial sector also raises ethical and regulatory issues. A key risk is unintentional discrimination through biased algorithms[8][12]. If AI systems are trained with biased historical data, they can reinforce existing inequalities, e.g. in the granting of loans.

Data protection and security are also critical issues[12]. AI models require large amounts of customer data, some of which is sensitive. Financial institutions must ensure that this data is collected, stored and used in accordance with applicable regulations such as the GDPR.

In addition, many AI models are non-transparent and difficult to understand ("black box" problem)[8]. This makes it difficult to check conformity with legal and ethical standards. Regulatory authorities are therefore increasingly demanding the use of explainable AI systems[17].

The introduction of AI harbours enormous potential for the financial sector, but also presents companies with major challenges. In addition to overcoming technical hurdles in data integration and model selection, ethical and regulatory aspects need to be taken into account. Only if financial institutions master these challenges and handle AI responsibly will they be able to fully utilise the opportunities offered by the technology. This requires close cooperation between industry, academia and regulatory authorities[7][11].

This KIROI masterplan provides a comprehensive approach to implementing AI in the financial sector. By applying the KIROI steps in a structured way, companies can ensure that all levels of the organisation are prepared for the use of AI and can use these technologies effectively.

Find out more on KIROI.ORG