One of the major challenges in adopting AI technologies in banking and finance is getting the required data in sufficient volumes and quality to train complex machine learning models. The financial services industry handles highly sensitive customer data related to transactions, investments, loans etc. Banking regulations like GDPR impose strict rules around how customer data can be collected and used. Getting the consent of customers to use their transaction data for training AI systems at scale is a big hurdle. Historical internal banking data may not always be complete, standardized or labeled properly for machine training. Cleansing, anonymizing and preparing large datasets for AI takes significant effort.

Another challenge is integrating AI systems with legacy infrastructure. Most banks have decades old mainframe and database systems that still handle their core functions. These legacy systems were not designed to support advanced AI capabilities. Connecting new AI platforms to retrieve, process and feed insights back into existing operational workflows requires extensive custom software development and infrastructure upgrades. Testing the integrated system at scale without disrupting live operations further increases costs and risks of implementation.

Hiring and retaining skilled talent to develop, manage and maintain advanced AI systems is also difficult for banks and financial firms. There is a worldwide shortage of professionals with deep expertise in fields like machine learning, deep learning, computer vision, and natural language processing. Competing with well-funded technology companies for top tier talent makes it challenging for banks to build dedicated in-house AI teams. The highly specialized skill sets required for building explainable and accurate AI further reduce the potential talent pool. High attrition rates also increase employment and training costs.

Ensuring explainability, transparency, accountability and auditability of automated decisions made by “black-box” AI algorithms is another major issue that limits responsible adoption of advanced technologies in banking. As AI systems make critical decisions that impact areas like loan approvals, investment recommendations and fraud detection, regulators expect banks to be able to explain the precise reasoning behind each determination. Complex deep learning models that excel at pattern recognition may fail to provide a logical step-by-step justification for their results. This can potentially reduce customer and regulator trust in AI-powered decisions. Trade-offs between performance and explainability pose difficult challenges.

Implementing advanced AI also requires significant upfront investments and long payback periods which discourage risk-averse banks and financial institutions. Costs related to data preparation, custom software development, AI infrastructure, specialized recruitment and ongoing management are huge. Clear business cases demonstrating ROI through quantifiable metrics like reduced costs, increased revenues or better risk management are needed to justify large AI budget proposals internally. Benefits accruing from initial AI projects may take years to materialize fully. Short-term thinking in the financial sector hinders committment of capital for disruptive initiatives like AI with long gestation periods.

Change management complexities is another hurdle as AI transformation impacts people, processes and culture within banks. Widespread AI adoption may cause jobs to be displaced or redefined. Employees need to be retrained which needs careful change management. AI also changes ways customers are engaged, supported and served. Gradual evolution versus big bang changes and addressing organizational inertia, biases and anxieties around new technologies requires nuanced change leadership. Overcoming resistance to change at different levels hampers smooth AI transitions in banks.

Data sovereignty and localization laws further complicate deployment of advanced AI capabilities for global banks. Countries impose their own rules around where customer data can be stored, processed and who has access. Building AI solutions that comply with diverse and sometimes conflicting international regulations significantly increases costs and fragmentation. Lack of global standards impedes efficient scaling of AI policies, models and platforms. Geopolitical risks around certain technologies also create regulatory uncertainties. Navigating the complex legal and compliance landscape poses major administration overheads for international banks.

Key barriers in applying AI at scale across the banking and finance industry include – lack of high quality labeled data, integrating AI safely with legacy systems, finding and retaining specialized skills, ensuring transparent and trusted decision making capabilities, securing large upfront investments with long paybacks, managing organizational change effectively, and complying with diverse and evolving regulatory requirements globally. Prudent risk management is important while leveraging AI to tackle these multidimensional challenges and reap the promised benefits over time.