Financial Services Will Embrace Generative AI Faster Than You Think

According to a McKinsey study, successfully implementing Generative AI in banking can add up to $340 billion annually. ¹ 

Paired with human expertise, Generative AI enhances performance and supports resource optimization, helping financial service providers be more agile, customer-focused, and resilient. The technology quickly processes information and can be trained to meet specific business requirements.

Generative AI financial services use cases

For instance, in customer service, Generative AI powers bots and virtual assistants that help employees respond to customers faster and more accurately. Tools like AssistAI automate the customer experience securely by providing the organization-specific information representatives need to address customer queries. By reducing the time spent manually searching knowledge bases, teams can focus on more personal interactions with customers and resolve issues the first time. 

As one of our early adopters, MoneyLion, a leading fintech company, has realized benefits such as improved average handle times and higher customer satisfaction scores. 

CASE STUDY

MoneyLion Ramps Up Generative AI-powered Tool for Teammates to Improve CX

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Credit evaluation is another use case for GenAI. Algorithms can quickly process financial data and analyze an applicant's credit history, income, and debts to assess creditworthiness, reducing wait times for loan decisions. Not only does this ability enhance the customer experience, but it also helps lenders manage risks more efficiently.

CASE STUDY

Automated Loan Processing System Boosts Efficiencies and Approval Rates for a Leading Financial Firm

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In addition, Generative AI can be applied for fraud detection, enabling financial service providers to prevent fraudulent transactions in near real-time. GenAI uses advanced pattern recognition and predictive features to spot unusual transactions so human experts can make informed judgments quickly.  

GenAI can create large amounts of synthetic data for AI model training, eliminating the need to use personally identifiable information and protecting personal data privacy.

CASE STUDY

Fraud Prevention and Transaction Monitoring for A Cryptocurrency Exchange

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What’s needed to adopt Generative AI

While Generative AI offers promising benefits and business value, internally implementing it isn’t easy without the right knowledge and resources.

To start, Generative AI applications may not be fully compatible with legacy financial systems, which can potentially cause disruptions and a dip in efficiency. Introducing technology into an organization requires change management, and people, processes, and technologies must adapt to the changes. 

Don’t forget to consider the risks

Businesses must also know the potential risks if Generative AI is incorrectly implemented.

Data privacy and security are paramount, especially in financial services. Yet, Generative AI needs data to learn. Protecting information and optimizing AI must be balanced and take skilled practitioners.

There is also the danger of bias or false information. Improperly trained or unsecured GenAI models could generate discriminatory and inaccurate outputs.

Intellectual property issues are another concern. If proprietary data is misused or if a model generates copyright-infringing outputs, legal challenges, and financial liabilities could ensue.  

Building the right Generative AI implementation strategy

TaskUs provides deep category expertise in building and integrating GenAI technology into your operations. We help companies like yours improve efficiency, strengthen compliance, and protect against fraud with a skilled team of fintech experts empowered with AI. Here’s how TaskUs can help:  

1. Create a strategic roadmap: We develop a detailed plan based on your business goals, opportunities, and timelines.
2. Focus on talent and knowledge: We recruit specialists and train current staff to develop mastery of tools and become subject matter experts. 
3. Invest in a strong data ecosystem: We build robust storage, management, and analysis systems to ensure the model generates accurate outputs. 
4. Prioritize fairness and compliance: We conduct regular audits, continuously fine-tuning your model, and monitor regulations to maintain customer trust and avoid legal issues
5. Implement a customer-centric approach: We tailor your GenAI model based on your specific needs

With a strong track record in the industry, we have been recognized as a Leader in the Everest Group’s PEAK Matrix for Financial Crime and Compliance (FCC) Operations Services. We are committed to helping your business innovate and achieve positive, tangible results.