In the past few years, the global financial technology industry has been greatly impacted by the emergence of artificial intelligence (AI) and machine learning (ML).
As the industry becomes increasingly automated and digitally transformed, AI and ML technologies are being utilized to build various financial services, including secure digital transactions and personalized financial advice. According to industry data, the global market size for AI in fintech is expected to reach USD 26.67 billion by 2026, with a compound annual growth rate of 23.17% from 2021 to 2026.
Here are some of the top AI trends in fintech industry to look for in 2023:
The machines will take over humans (literally) – The rise of multilingual conversational chatbots.
In the coming months, we will see that chatbots use machine learning and AI to handle frequently asked questions from customers in their local languages and dialects, which call centers often spend a significant amount of time and resources answering.
By analyzing big data, fintech companies can identify specific customer queries and interaction patterns and use this information to train their chatbots. In addition to answering pre-programmed questions, sentiment analysis allows chatbots to understand customers’ relationships with financial services, leading to improved customer satisfaction and process automation.
AI will take the centre seat in the decision-making process
Credit decisioning is often one of the first areas to adopt AI. According to McKinsey, AI works with structured and unstructured data to make more precise credit decisions and positively impacts the credit approval process, including the turnaround time and the percentage of applications approved.
There are three main ways in which AI-driven credit decisioning will be adopted in 2023:
- Credit qualification: AI will analyze many consumers and accurately determine whether a particular client is eligible for a loan
- Limit assessment: AI algorithms will automate determining the maximum borrowing limit based on various factors
- Pricing: AI-powered fintech can offer competitive rates and adjust pricing based on market shifts
Conventional credit risk analysis often uses complex statistical models that assume formal relationships between features in the form of mathematical equations. In contrast, artificial intelligence (AI) uses machine learning (ML) methods that can learn from data without requiring rule-based programming. AI uses several types of classical ML algorithms and deep learning techniques, including Random forest method, Support vector machine (SVM), K-nearest neighbors (KNN), and Neural networks (NNs)The role of CDO/CDS will become more prominent
As fintech companies seek to make data a more central part of their operations, the role of the Chief Data Officer (CDO) or Chef Data Scientist (CDS) is becoming increasingly strategic. The CDO/CDS is responsible for providing insights that enable data-driven decision-making and for helping to assess the impact of data on the organization’s overall functioning. Currently, there is yet to be an established framework for this assessment, but the CDO/CDS are well-positioned to take on this role.
Deploying data at scale and improving technical maturity can have several implications. The CDO/CDS will play a crucial role in aligning these efforts with the evolving regulatory landscape and helping to establish an open and transparent data culture that transcends organizational and functional silos.
Defi and blockchain will be back with a bang
Decentralized finance (DeFi) is an emerging financial technology that is based on secure distributed ledgers, similar to those used by cryptocurrencies.
Decentralized finance (DeFi), which aims to make peer-to-peer transactions instant and free, addressing the current challenges of costly, slow international payments is expected to be back in vogue.
DeFi’s truest potential to serve users lies in its ability to serve applications, as well as ecosystems. Additionally, it is the versatility of DeFi legos that makes them the perfect infrastructural tools to power Web 3.0 ecosystems that need trustless, on-chain financial services and deep liquidity.
It may seem like AI is a future technology, but it has been around for over 50 years. The first AI was introduced in 1956, and now is the time for businesses to fully understand its potential and use it globally. AI can make Fintech firms more successful, and companies that do not adopt it may struggle to keep up with competition. Getting on board with AI now will give them a competitive advantage and allows them to get a head start in the race. Also, with AI, companies will be able to build best-in-class fintech products that are new-age, safe and secure.
The author is Chief Data Scientist at BharatPe.
Disclaimer: The views expressed are solely of the author and ETCIO.com does not necessarily subscribe to it. ETCIO.com shall not be responsible for any damage caused to any person/organization directly or indirectly.