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‍‍Machine learning and artificial intelligence in fintech: the next wave of financial innovation

‍‍Machine learning and artificial intelligence in fintech: the next wave of financial innovation

Artificial intelligence (AI) and machine learning (ML) are no longer experimental technologies in the financial space. They're foundational pieces of companies' efforts to drive innovation and enable whole new business models.

Digital product development leaders in fintech need to understand how to strategically apply AI and ML to stay ahead of the competition in the years ahead. With that in mind, it's worth exploring real-world opportunities and tangible applications associated with AI and ML across several functional areas in fintech.

Enabling personalization at scale with AI and ML

One of the hallmarks of AI and ML is the ability to synthesize appropriate, automated responses from vast data sets. Customer interactions provide the perfect raw material for this purpose, unlocking new personalization options.

Hyper-personalized banking experiences

With AI, fintech companies can personalize a variety of services, from financial advice to product recommendations, and offer individualized budgeting tools. Businesses that explore AI-driven personalization can increase customer engagement, build loyalty, and unlock new cross-selling and upselling opportunities.

Current examples of personalized banking include An ML algorithm that analyzes customer behavior in real time, allowing banks to provide personalized advice to each of those consumers. Generative AI (GenAI) can further enhance such a program by generating custom reports and insights that automatically summarize complex financial data in a personalized format.

Next-generation customer service

Chatbots and virtual assistants have been some of the longest-standing and most mature examples of AI technology in action. Today, these tech tools can generate instant, personalized responses to queries. Powered by the latest generation of GenAI, chatbots can create more natural and contextually relevant answers than ever before.

Fintech companies that implement advanced natural language processing (NLP) models, enhanced by GenAI, can satisfy customers who want to complete simple interactions quickly, freeing up teams to focus on higher-value tasks.

Bank of America's Erica chatbot exemplifies a new group of powerful tech tools that can handle tasks ranging from managing accounts to dispensing investment advice. Augmenting these tasks with bots reduces wait times and boosts customer satisfaction.

Transforming risk management and fraud detection

Fraud and cybercrime present moving targets for financial institutions, as attackers are always updating their methods. AI and ML provide a toolset that can enable businesses to stay current and protect themselves.

AI for dynamic risk assessment

Fintech organizations control vast data sets. By analyzing this information with AI and ML algorithms, it's possible to engage in real-time dynamic risk assessment. These algorithms can evolve to suit market conditions faster than legacy technology could. Businesses that leverage AI can create more accurate, adaptive risk models that evolve alongside market data and enhance decision-making around credit and loan approvals.

When using AI for decision-making support, whether that means approving loans or assessing creditworthiness in general, fintech companies must consider the risks of bias in AI models and ensure the transparency and accountability of their offerings. Decision-making processes must remain explainable by humans and compliant with industry regulations.

For an example of this tech in action, AI-powered credit scoring models use alternative data sources to create a more holistic view of a customer's creditworthiness than would be possible with conventional metrics.

Real-time fraud detection and prevention

AI-powered fraud detection systems analyze real-time data — thousands of transactions per second — to determine suspicious patterns and preemptively stop fraud. Fintechs with AI-driven fraud prevention systems can protect themselves more effectively against new and evolving tactics and schemes. The solutions automatically adapt to respond to emerging styles of fraud.

PayPal uses ML as part of its defensive systems, monitoring and flagging fraudulent transactions across its vast, global payments network.

Enhancing algorithmic trading and investment strategies

Detecting advanced market signals and acting on them at high speed have been algorithmic functions for years. The latest AI and ML tech tools stand to make these processes even quicker and more effective.

AI-enhanced algorithmic trading

ML models can update and empower algorithmic trading. Large data sets dealing with financial, social, and even geopolitical factors are now available in real-time, allowing fintech organizations to optimize trading strategies. Fintechs involved in wealth management and trading can adopt AI-based models to respond more quickly to market fluctuations. This has the potential to improve predictive accuracy and increase returns.

As with the AI tools used to assess creditworthiness risk, there’s a need to audit algorithmic trading models for bias and transparency. Users of these models must make sure the tools provide clear, explainable outputs and monitor their accountability. This need for caution is due to the financial implications of automated trading decisions, and is especially acute in volatile markets.

Today, hedge funds are increasingly using AI-powered algorithms to predict market trends by analyzing real-time news feeds, social sentiment information, and historical trading data. GenAI can assist in this process by creating predictive models and refining trading strategies.

Robo-advisors for automated investment

Digital advisor algorithms are fully automated programs that offer portfolio management solutions based on an individual client's financial goals and risk profile. By providing these services affordably and accessibly, they help democratize investment.

Fintechs that use AI-powered robo-advisors can expand service offerings and serve a broader audience by introducing accessible, low-cost investment options that don't require extensive hands-on attention.

When companies use ML/AI to create and manage portfolios, it's important to ensure those models are transparent. The AI models must be explainable and regularly audited for bias to ensure that the models they generate for clients really do align with their goals and risk tolerances.

Driving operational efficiency with AI and ML

In addition to client-facing AI and ML deployments and ones that directly target a financial organization's core strategies, there are other opportunities to apply the technology. This encompasses the backend processes and operational matters that can be streamlined through the application of modern algorithms.

Automated compliance and regulatory reporting

Introducing AI into regulatory technology (RegTech) enables organizations to manage their compliance and reporting more efficiently. The modernized systems enable quick detection of compliance issues while streamlining reporting in quicker and less expensive ways.

Organizations using AI platforms can generate regulatory reports, flag any detected discrepancies, and suggest corrective actions. This enables them to remain compliant and reduce manual effort expended.

In addition to streamlining reporting and compliance processes, fintechs can generate regulatory documents and reports with AI assistance, ensuring they efficiently adapt to changing regulations.

AI for back-office automation

There are numerous time-consuming backend functions at play in any fintech. These include payment processing, loan origination, and transaction settlement. Automating these functions with AI-powered systems reduces processing time while also cutting the risk of human error.

Introducing ML into payment gateways allows organizations to detect and rectify transaction anomalies in real time, delivering increased speed and accuracy for financial processes. AI-based automation in loan verification and approval processes can also decrease the time needed for processing.

Organizations that automate multiple labor-intensive back-office processes with AI can reduce operational costs, improve accuracy, and make their operations more scalable. New AI solutions can also take on mundane, repetitive tasks such as customer onboarding or routine account updates, freeing up employee time for more strategic activities.

AI-driven workflow optimization

By applying AI and ML to internal workflows, financial organizations can identify inefficiencies, bottlenecks, and opportunities for improvement. Using these systems to optimize task allocation and resource management, fintechs can maximize productivity and ensure their teams work on high-value tasks.

AI-based tools exist to prioritize and automate routine tasks such as scheduling, reporting, and email triaging. These solutions relieve administrative burdens and allow employees to focus on strategic decision-making.

When organizations integrate AI-powered workflow tools, they can improve productivity across departments, enhancing overall operational efficiency and minimizing unproductive downtime.

The new era of innovation in fintech

The strategic application of AI and ML in fintech is opening new opportunities for innovation. A new era of hyper-personalization, operational efficiency, and enhanced risk management is beginning, spurred by this technology.

As fintech companies continue to integrate AI into their operations, the ability to strategically leverage these technologies, including recent innovations like advanced GenAI, will be the key to staying ahead in a competitive space. Transcenda's AI and data science expertise can help businesses capitalize on opportunities that come their way as they drive innovation and long-term growth.

Contact Transcenda to learn how we can help you design and develop the next generation of fintech solutions.

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Tom Madzy

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