The world of money is changing fast. At the centre of this transformation is something that used to sound like science fiction: artificial intelligence in finance. AI isn’t just the latest tech trend. It’s reshaping everything we do, from how we manage investments and bank accounts to how companies manage complaints and make split-second decisions.
AI and finance are becoming increasingly intertwined globally. As of 2024, 72% of finance leaders used AI in their operations. Soon, those not getting involved will be a dwindling minority.
Companies like Proto are already helping financial institutions like digital banks and crypto exchanges use AI in real-world, everyday situations. These innovators are powering multilingual chatbots that help people track remittances in the Philippines and AI assistants that solve app issues for users in Uganda. The story for AI in finance isn’t just about automation or algorithms. It’s about trust, local understanding, and improving processes for everyone.
What is AI in Finance and why does it matter today?
So, what are industry experts talking about when they discuss AI in finance?
Ultimately, AI in the finance industry is all about using machines and software solutions to do things that would otherwise require human brainpower. AI applications use natural language processing, machine learning algorithms, and neural networks to handle tasks.
Those tasks might include answering a customer's question, spotting a suspicious transaction, or predicting what will happen to the market next week.
But here’s the difference between modern artificial intelligence in the financial sector and the tech it’s replacing. Today’s AI doesn’t just follow instructions. It learns. It adapts. It figures things out. That’s what makes it so powerful and valuable for the fast-paced world of finance.
Take Proto’s customisable AI assistants for the finance industry. They help people navigate real banking issues in their local language. This is crucial for organisations like the Central Bank of the Philippines, which must provide broad access to high-quality financial services.
Proto’s AI engine is trained on local data, meaning it can tell the difference between Tagalog and Spanish. That’s something even the biggest large language models like ChatGPT can sometimes get wrong. More importantly, solutions like this don’t push humans out of the picture; they pull them in.
Financial agents can train and improve the AI using plain English, adjusting the assistant to fit real-world needs. So instead of replacing people, the AI becomes a tool to make teams more effective.
The state of AI for finance: From hype to operational core
Remember when AI in finance was a hyped-up trend? Today, it’s a fundamental part of how the industry operates. In one EY survey, 99% of financial services companies said they were using AI. All respondents said they were using or planning to use GenAI already.
Although many companies still face challenges with artificial intelligence in finance, like navigating compliance and security risks, the benefits of adoption are clear.
IBM shared that 67% of leaders say AI solutions have helped boost their revenue by 25% or more. Another 66% said AI boosted their profit margins by up to 25%.
But it’s not just that AI in financial services, banking, and accounting makes companies more profitable. The benefits of AI in finance go a lot further. The right tools can transform the fabric of financial operations. They can enhance customer experiences, improve employee efficiency, streamline compliance processes, and more.
Proto’s AI-powered assistants are already supporting financial institutions in emerging markets. In Zambia, their AI assistant helps commercial bank automate 80% of responses to common customer queries. In the Philippines, Proto powers an assistant that handles 60,000 interactions annually, adapting to each user’s language and specific needs.
AI applications in finance: Top use cases
AI isn’t just being plugged into finance for the sake of it. It’s showing up exactly where it makes the biggest difference, from front-line customer service to back-end compliance systems. The results are already incredible.
AI in finance customer support & omnichannel experiences
If there’s one area where AI in finance shines for everyday customers, it’s in support. Today’s customers expect quick answers, helpful guidance, and zero complexity, and AI agents in Finance deliver just that.
Proto’s work with USSC in the Philippines is a perfect case study. This financial services provider rolled out a multilingual AI assistant that now handles a huge range of user needs, right inside of messaging apps like Facebook Messenger. It can speak the language of the customer, and address a range of issues, such as:
- Complaint filing. If a customer has an issue, the assistant guides them through the reporting steps. If they see a suspicious transaction, the bot gathers the details and automatically creates a ticket, handing it to the relevant team for investigation. It even routes complaints about specific services, like U Visa or U Business Solutions, to the right department without delay.
- Remittance Support: Sending money is one of USSC’s core services; the assistant walks users through how to do it online. It also provides real-time tracking updates via API integration, and if someone wants to cancel or change a transaction, the bot kicks off the ticketing process for that, too.
- Mobile App assistance: This is where the assistant handles a high volume of everyday queries, such as how to reset a password, fix login issues, or use mobile wallet features. These automated FAQs free up human agents to focus on more complex problems.
- Automated ticket creation: This pulls the whole experience together. If the assistant can’t solve an issue directly, it creates a pre-filled ticket with all the necessary context. That way, the human agent doesn’t need to ask the customer to repeat themselves. It’s fast and efficient, and it's what good AI for finance teams should look like.
This isn’t about replacing people, either. Proto’s platform gives agents full control to fine-tune the assistant in plain English, making sure the AI continues to reflect real customer needs.
AI for finance risk management & fraud detection
If AI shines in customer support as the friendly face of modern finance, it is in risk management where it becomes the unseen sentinel – watching, learning, and responding faster than any human team could.
In today’s high-speed financial systems, milliseconds matter. That’s why forward-looking institutions increasingly rely on AI to monitor transactions in real time, using anomaly detection and behavioural analytics to flag activity that deviates from the norm – whether it’s a login from an unfamiliar location, a suspicious cluster of purchases, or an abrupt spike in withdrawals. These systems don’t just follow rules – they learn patterns of behaviour and raise alerts the moment something looks off.
But detecting fraud is only half the battle. The real race begins once a scam occurs. The likelihood of recovering stolen funds drops sharply after just 24 hours, yet many victims hesitate to report incidents – often due to language barriers, stigma, or lack of access to reliable reporting channels.
This is where AI’s role expands from surveillance to empowerment. Proto’s multilingual AI assistants are built to reduce this friction. They meet victims where they are – on familiar platforms, in local languages, and through fully anonymous channels. From suspicious transaction reports in Taglish in the Philippines to consumer complaints in Kinyarwanda in Rwanda, Proto ensures that every voice can be heard and acted upon.
These AI assistants don’t determine the outcome of a fraud investigation, but they make sure issues reach the right hands – fast. They collect and escalate fraud-related complaints automatically to the appropriate teams. This not only improves transparency but significantly reduces response times.
Through a strategic partnership with fraud analytics firm FNA, Proto’s impact now goes further. FNA’s Money Trails platform – already deployed by Bank Negara Malaysia – enables financial authorities to trace illicit fund flows across networks in just 30 minutes. Integrated with Proto’s AI assistants on the front end, this joint solution will deliver a full workflow for fraud response: from the first complaint filed by a citizen to the forensic tracing and resolution of financial crimes.
AI in financial planning & forecasting
Behind every confident CFO is a mountain of financial data. These days, AI in finance is making sense of that mountain faster than ever.
The days of clunky spreadsheets and static dashboards are done. Today, AI in financial services is powering dynamic forecasting models that simultaneously simulate thousands of scenarios. Whether modelling market volatility, predicting future cash flow, or stress-testing investment strategies, AI gives finance leaders a real edge.
Machine learning models thrive on patterns. They can digest years of financial data, detect seasonality, spot outliers, and even account for macroeconomic factors, all in a fraction of the time it would take a traditional FP&A team. Combined with natural language processing, these tools can pull insights from unstructured sources, like earnings calls or analyst reports.
This isn’t just about saving time. It’s about making better decisions. Companies now use AI for finance and accounting to shift from reactive planning to proactive strategy. Instead of waiting for quarterly results to adjust course, finance teams can act in real time.
While Proto’s assistants aren’t designed to replace financial analysts, they do support this shift toward smarter finance by automating some of the data-gathering tasks that often slow teams down
AI in banking and finance compliance & regulatory reporting
Ask any financial institution what causes them the most stress, and “compliance” will probably make the list. The rules are complex, the stakes are high, and the paperwork can be endless.
That’s where AI in accounting and finance is stepping up, not just to keep things compliant, but to keep them efficient. AI applications in finance now include tools that automatically read, interpret, and apply regulatory rules using natural language processing. This saves hours of human review and reduces the risk of missing something critical.
Proto’s work in this space shows what responsible AI looks like. Take the Philippines' financial consumer protection project, where Proto built an AI assistant that helps users file complaints directly with the regulator. It automatically collects information, creates a ticket, and routes it to the right financial institution.
In Rwanda, the National Bank uses a similar setup, with Proto’s platform giving the bank complete visibility into incoming complaints. This allows them to monitor resolution timelines, mediate disputes, and protect consumers, all with a digital-first approach that’s faster, fairer, and more transparent. These cases show how AI in the financial sector can support compliance, build trust, and protect users.
Stakeholders of AI in finance: Who drives the change?
AI isn’t something that just sneaks into a business unnoticed, especially not in finance. The decision to bring in artificial intelligence often starts at the top. CIOs, CFOs, chief digital officers, heads of operations. These are the people setting the vision. But it’s not just about vision anymore.
It’s about outcomes.
Across the board, financial leaders are asking one big question: What’s the ROI? It’s not enough for AI in finance to be shiny or new; it has to work quickly.
That’s why many financial institutions partner with external vendors like Proto instead of trying to build their own AI systems from scratch. Proto gives banks and fintechs a platform they can launch quickly without hiring an internal AI team or risk exposing sensitive user data to black-box systems. Everything from multilingual support to complaint automation is ready to go, and it’s built to scale securely.
This collaborative model is especially helpful in regions where budgets are tight, but the need for transformation is high. For stakeholders trying to modernise without overextending, having a reliable, compliant, AI-powered partner in their corner makes all the difference.
Mastering AI governance in financial services
As AI becomes more embedded in the financial industry, it’s not just about what the tech can do. It’s about how it does things. That’s the challenge of AI governance.
Finance companies, more than most sectors, have to be cautious. There are strict rules around privacy, auditability, and fairness to follow. Customers trust these institutions with their money and their data, so when it comes to deploying AI in financial services, responsible design is a must.
That’s where Proto really stands out. Unlike many AI providers that lean heavily on large language models like ChatGPT, Proto limits its reliance on those systems for a simple reason: data privacy.
Instead, Proto’s own NLP engine handles most conversations, especially in underserved languages, and only hands off to third-party models when absolutely necessary. This reduces exposure to external servers and helps keep personally identifiable information (PII) secure.
Proto is built for audit readiness. The platform is backed by SOC 2 and ISO 27001 certifications, meaning it meets some of the highest standards in data security and process integrity.
Use cases and success stories in AI for finance and banking
It’s one thing to talk about AI's potential in finance, but the real proof comes from what’s happening worldwide. Banks and fintech companies already use AI to scale operations, support customers, and create more inclusive financial systems.
Digital banks leveraging AI at scale
Digital-native banks are quickly embedding AI in financial services. These institutions have been building operations around intelligence from day one.
Take mobile-first banks that rely on AI to handle onboarding, KYC checks, loan approvals, and customer service. Instead of running these processes in separate silos, everything is connected through AI-powered platforms that learn and adapt in real time.
Companies like Sparkle are even using Proto AI to automate 76% of customer support requests, ensuring customers get the streamlined experiences they need faster, in their own language.
When someone opens an account or needs help navigating a mobile app, AI provides intelligent support, whether that’s guiding them through features, answering questions, or flagging a risk pattern that needs human review.
Proto’s role in emerging markets
While many AI solutions focus on big, global markets, Proto doesn’t discriminate. This company supports finance in emerging economies, where language diversity, infrastructure gaps, and regulatory sensitivities can complicate digital transformation.
We’ve already mentioned Proto’s work with USSC, which has scaled to handle 30% more inquiries, without increasing headcount, just by experimenting with AI applications.
FNB Zambia uses a Proto-powered AI assistant to handle customer queries across social channels like WhatsApp and Facebook in various languages. This gives users a seamless way to ask for help, check balances, and file service complaints, all without leaving the platforms they already use daily.
These stories show how AI in the finance industry is reshaping banking in places where access has historically been limited and making financial services more human, local, and responsive.
AI for financial inclusion
In many parts of the world, the biggest barrier to financial access isn’t money; it’s infrastructure. People may not have a bank branch nearby or a stable internet connection, but they do have mobile phones. And that’s where AI in finance can be transformative.
Proto’s work in the Philippines is a perfect example. A Fintech provider in the Philippines uses a multilingual AI bot to boost financial inclusion, surging towards bringing at least 70% of the nation into digital banking.
In the crypto space, Proto has supported exchanges in building AI assistants that protect traders by automating onboarding, FAQs, and issue escalation. This ensures people new to crypto aren’t left guessing; they get the help they need, fast.
Barriers, biases, and ethical challenges of AI in finance
AI might be powerful, but it isn’t perfect. In the world of finance, even small mistakes can have big consequences. One of the most pressing challenges is algorithmic bias.
AI learns from historical data. A model could act up if the data reflects past inequalities, like discriminatory lending patterns or exclusionary policies.
Another issue is explainability. Many AI applications in finance operate like black boxes: they make decisions, but can’t clearly explain why. In finance, that’s a problem. Regulators, auditors, and customers all need transparency.
Data privacy is also a big concern. Who has access to sensitive financial information? Where is it stored? How is it used? Companies like Proto stand out in this area. Proto’s platform is designed with privacy and compliance at its core, limiting exposure to large general-purpose models and giving financial institutions control over where and how data flows.
One final issue: scaling. Legacy infrastructure, siloed data, and strict regulatory frameworks make large-scale AI adoption a challenge. That’s why scalability, flexibility, and cloud readiness are essential when choosing an AI solution. Modern platforms for AI in finance, like Proto, are built with this in mind.
Proto’s platform is API-first, meaning it can connect with a bank’s current CRM or support ecosystem with minimal disruption. Whether pulling data from internal systems, escalating tickets, or syncing with reporting tools, Proto is built to integrate, not isolate.
This matters because the future of AI in finance isn’t a single tool; it’s an ecosystem. AI must talk to other systems, update in real time, and scale as the business grows.
The future of AI in finance: What’s next?
AI is already reshaping the present of finance, but there’s room to grow.
We’re moving beyond simple bots and chat interfaces into the age of generative AI, agentic systems, and AI copilots. These are intelligent tools that don’t just respond to commands but proactively assist. In finance, this could mean AI tools that help build investment strategies, write reports, flag regulatory risks, and serve as real-time assistants during customer service calls.
For CFOs, AI will likely evolve from a back-office tool into a strategic co-pilot. Tools will offer forecasting advice, highlight anomalies, and suggest ways to optimise performance in real time. And for smaller institutions, especially in emerging markets, these tools could level the playing field.
Proto’s roadmap is moving in this direction, too. The company is expanding its assistant capabilities to support more advanced customer interactions and workflows. Already, Proto gives companies the flexible tools they need for ticket management, analysis, inbox automation, and more. Plus, it empowers companies to embrace omnichannel experiences, which are crucial today.
A strategic mandate, not just a technology trend
The days of AI being treated as a tech experiment in finance are over. AI isn’t a side project anymore, it’s a strategic mandate. Whether it’s streamlining customer service, spotting fraud in real time, or powering predictive forecasting, artificial intelligence in finance is shaping how institutions operate, compete, and grow.
The winners in this space won’t just be the ones who move fastest. They’ll be the ones who build responsibly, prioritise privacy, invest in explainable systems, and partner with platforms like Proto, which understands that local language support, security, and human-in-the-loop design are essentials.
For banks, fintechs, and regulators alike, the future of AI in the financial sector isn’t just about automation. It’s about building better, smarter, and more inclusive financial systems.
FAQs
What is artificial intelligence in finance?
It’s where smart technology meets money. AI in finance uses machine learning, predictive analytics, and natural language processing to manage fraud detection, customer service, and forecasting tasks.
How is AI used in finance?
Pretty much everywhere. From chatbots that answer your banking questions in seconds to systems that scan millions of transactions for fraud, AI in financial services is working quietly in the background to keep things running smoothly.
How is AI transforming finance processes?
AI is the ultimate multitasker. It streamlines routine workflows, automates support, and delivers insights faster than any analyst ever could. Platforms like Proto take this further by turning multilingual, omnichannel customer support into a fully automated, always-on experience.
How will AI affect the finance industry and jobs?
It’s not about robots taking over. It’s about people getting superpowers. AI in finance will shift teams away from repetitive tasks and toward strategic thinking. Tools like Proto’s platform don’t replace human agents; they augment them.
