Cloud-based Deep Learning:AI Applications in Finance

Advanced AI Applications in Finance

AI's pervasiveness is becoming second to none, as it's altering financial operations even in the most traditional areas. The following are some of the advanced applications of AI in finance:

Credit Assessment

Methods used in traditional credit assessment frequently rely on manual labor and past information. So, errors seeping in is common, something that's detrimental to service quality on the customer end and risk management on the part of the lender.

Kevin Levitt, Nvidia's Head of Global Industry Business Development for the Financial Services industry, says that in the fintech industry, "It's about how many different variables and insights can you leverage in making a decision around customer acquisition, customer servicing, and helping them along their financial journey."

His views come in response to the possibilities of AI's inclusion in the credit assessment and risk management landscape. As it stands, financial companies can more precisely and quickly determine a person's credit risk by using AI. These algorithms consider alternative data sources, such as social media activity, transaction trends, and even smartphone usage, in addition to traditional credit data.

Also, lenders can expedite loan applications and anticipate delinquencies months in advance by using AI — all while ensuring that a superior customer experience is facilitated.

Fraud Detection

A recent analysis reveals that 36% of financial institutions experienced heightened card fraud in 2022. This was 10% more than what was observed in 2021. Banks and financial institutions also suffered from high costs of compliance, cyberattacks, internal workflow errors, and more.

The question is — can financial institutions address the challenges associated with different fraud types (phishing, fraud-as-a-service, BEC, etc.) using AI?

As it turns out, they can! Financial institutions can stop fraudulent transactions before they happen. They can leverage machine learning models' ability to spot suspicious behavior and anomalies. AI-driven systems continuously learn from fresh data. These systems can adjust themselves to changing fraud tactics and, as such, reduce the risks.

Computing for Insurance

The insurance sector offers a great avenue for AI to exercise its potential. For example, AI chatbots and virtual assistants provide round-the-clock customer service, answering questions, assisting clients with filing claims, and improving general customer happiness.

Moreover, AI streamlines the underwriting process by automating data analysis, accelerating policy approvals, and minimizing the need for administrative intervention. Insurers can proactively customize insurance products and services to match client demands using AI to forecast customer behavior and spot potential risks.

Considerations for Successful AI Implementation

It's essential that financial institutions know the implications as well as considerations before jumping on to the AI-enabled bandwagon:

Enabling Effective Explainability and Interpretability

Although AI models can produce spectacular outcomes, users and stakeholders frequently perceive their internal workings as a "black box." They find it difficult to gauge the value they are provided. Businesses can make AI more transparent and understandable by devising strategies that give clear explanations of how the AI makes decisions.

When implementing AI algorithms in key applications, transparency is essential. Models should be created to offer concise justifications for their judgments and projections. This is crucial for trust and compliance in fields like healthcare and finance, where understanding the logic behind decisions is immensely critical.

Ensuring Data Privacy

Keeping data private is a crucial factor to take into account while implementing AI, especially as data becomes more valuable and prone to security breaches. The idea should be to:

Secure sensitive data from unwanted access
Maintain compliance with data protection laws like GDPR or CCPA,
Protect individual privacy rights

Here are some strategies for effective data privacy safeguards with AI:

Differential privacy is a method for offering aggregate insights without disclosing specifics about any individual's data by adding noise to individual data points. This strategy preserves people's privacy while enabling AI models to discover useful patterns.
Another privacy-preserving method is federated learning, in which AI models are trained locally on user devices or servers, and only the updated models, not the original data, are shared centrally. This decentralized strategy preserves privacy while limiting data exposure.

Optimizing Costs and Planning Budget with Cloud

A well-structured budget and cost optimization strategy are essential for the effective and long-lasting implementation of AI initiatives.

Scalability and flexibility provided by cloud-based services allow businesses to modify resources in response to demand and minimize costs while still achieving performance standards. Utilizing free and open-source AI frameworks and tools can also save the cost of software licensing.

Selecting the Right Cloud Service

When choosing a cloud service for AI adoption, it's important to consider:

Applicability to business requirements
Security and compliance mechanisms
Support to machine learning and deep learning frameworks
Service and infrastructure latency
Data lifecycle management
Service availability and uptime
Cost-effectiveness

In the past few years, the proverbial hype surrounding AI has solidified. At Wissen, we empower financial institutions to integrate AI into their businesses — bringing efficiency and profitability to the table. Get in touch to learn more.