How to Overcome AI Implementation Hurdles

Only 21% of organizations in the financial sector that have adopted artificial intelligence (AI) have established policies that regulate its use. Furthermore, few of these have adequately addressed some of the main risks associated with the use of AI: transparency, bias and explainability. Therefore, organizations must develop solid strategies to mitigate the risks inherent in adopting AI technologies for their operations.

One of the main challenges in the implementation of AI within companies, especially those in the financial sector, is that the regulation in this matter has not yet matched the continuous pace of technological innovation. In addition to the innovative aspects that result in a great area of opportunity for organizations, companies must be alert to the ethical, reputational and financial risks that not only a lack of regulation implies, but also to risk aspects such as those related to fraud and cybersecurity that, from crime, also evolve at a dizzying pace.

And with the revolution that we are experiencing today in terms of the implementation of AI solutions, many companies are turning toward these solutions and using them in different aspects of their operation. According to a McKinsey & Company study on AI-based risk management, in Latin America, 40% of organizations will increase their investment in these solutions due to the advances that generative AI has demonstrated.

AI-based risk management represents a significant evolution in the way organizations address and mitigate risks in and to their operations. This innovative approach harnesses the power of machine learning algorithms and advanced analytics to analyze large volumes of data, identify patterns and trends, and thus make informed decisions in real time.

Although the adoption of AI in risk management is developing in Latin America, it is important that financial institutions understand both the benefits and risks associated with them, as well as the gaps in the preparation of companies for the widespread use of AI in enterprise risk management.

Some benefits, challenges and risks to take into account:

Benefits

Reduce the risk of fraud: AI can detect patterns that humans might miss, helping prevent fraud in transactions, credit applications and insurance policies.

Improve decision-making: Analyzing large amounts of data to identify risks that may not be obvious to the naked eye allows financial institutions to make more informed decisions about granting credit, underwriting policies and managing investments.

Optimize efficiency: Advanced analytics can automate repetitive tasks, such as evaluating credit applications, freeing up time for employees to focus on more strategic tasks.

Personalize the customer experience: AI can be used to offer personalized financial products and services to each customer, based on their risk profile and specific needs.

Challenges and Risks

Lack of regulation: The rapid evolution of AI has outpaced regulators' ability to establish adequate regulatory frameworks. This can generate uncertainty and risks for financial institutions that already use AI or are in the process of implementing it.

Bias in the data: AI algorithms can reproduce existing biases in the data used for their training. This can lead to discrimination in decision-making, such as unjustifiably denying credit to certain groups of people.

Transparency: AI algorithms can be difficult, making AI decisions difficult to understand. This can generate distrust between users and authorities, as well as a lack of transparency.

For banks and insurers in Mexico, and the financial sector in general, it is important to be aware of the benefits and opportunities of using AI, but also the risks, in order to take advantage of these new technologies responsibly, as a large proportion of companies are focused on utilizing AI to improve digital customer experiences. In a recent survey by SAS, 43% of respondents cited the digital customer experience as a priority.

Findings in Mexico and Latin America

One of the firm's main findings is the willingness of banks to digitize their credit evaluation processes. There is a significant opening toward the adoption of technologies like  AI and machine learning to improve both the precision and efficiency in decision-making, such as the issuance of consumer credit (in the same survey, 36% of respondents consider speed of decision-making as a driver in the transformation of credit decisions).

In addition to the financial sector, other sectors that can benefit from adopting AI for risk management are those that are exposed to significant changes in the environment due to climate change, such as agriculture. The ability of AI to adapt to the specific risks of each sector, such as predicting weather conditions, optimizing the use of resources, and detecting diseases and pests, in the case of agriculture, and in fraud detection, early signals of stress, or market analysis, for the financial industry, means AI is becoming an essential tool for successful risk management.

Generative AI is frequently used to analyze or extract information from large sets of documents. For example, one of our clients used generative AI to quickly summarize 300- or 400-page regulatory documents, obtaining an accurate synthesis of the actions required by the regulatory documents.

Additionally, AI is used to detect fraud by analyzing patterns and anomalies in financial transactions, helping to prevent financial losses and protect the integrity of the banking system. There is a call for those responsible for risk management to consider the digital transformation and innovation of their credit evaluation processes as a priority. It is essential to have technologies that can adapt to market changes and guarantee agile and accurate decision-making.

The theme of innovation in risk management allows organizations to anticipate emerging challenges and proactively seize new opportunities. By adopting an innovative approach to risk management, organizations can improve their ability to identify and mitigate potential risks, as well as take advantage of new business opportunities that might otherwise go unnoticed with highly focused solutions.