The RBI (Reserve Bank of India) has taken huge steps to unravel the economic fundamentals of data, payments, and identity as India gets ready for the internet world. The inclusion of the Account Aggregator (AA) Framework is another significant step in this way.
Put forward as a permission or consent layer; the RBI Account Aggregator Framework targets to power up India’s data pyramid. It empowers people to share their information only when they permit it and when an electronic authorisation framework grabs it.
On the other hand, Artificial intelligence (AI) has become a transformative force, changing industries worldwide. In the financial industry, one feature of AI that has attained significant observance is Generative AI (GenAI). This technology is changing conventional banking and economic services by improving efficiency, decreasing risks, and offering a personalised customer experience.
Now, we will discuss in detail to get a clear view of both terminologies.
RBI Account Aggregator: Role and Framework
If you have many bank accounts, loans, and investments, it is tough to decrease everything to one number. That is solely one area where account aggregators can be helpful. With the help of the RBI account aggregator framework, customers can get access to all account details, such as tax, securities, pension, and insurance.
Account Aggregators perform the role of consent or permission manager. They keep a record of customers’ approval, which allows information to be allocated flawlessly. The consent information allocated by FIP is encoded and transferred to FIU after permission requests are received. Then, FIU decodes this detail for use in many ways that are adjourned with the customer.
Thus, the RBI Account Aggregator framework simplifies data-allocation processes, reduces data-gathering friction, and removes theft threats. Users may also directly communicate with the AA to take back their permission at their convenience.
RBI Account Aggregator Framework’s Workflow
This framework aims to enhance access to commercial details by constituting consumer information. With the user’s authority, they will collect and allocate their info from different entities. They keep customers’ details, called FIPs, to many entities asking for user info known as FIUs. For example, if a user requests a loan, the loan provider (an FIU) will require access to the user’s previous account statements, which are reserved by the bank (a FIP), to check the user’s credibility.
Use Cases of RBI Account Aggregator
1. Financial
This is one of the most important niches for using AA services. The AA framework has already been approved to benefit MSMEs and underserved people in the country.
2. Lending
In India, P2P lending, microlending, digital banking units (DBUs), POS (Point-of-Sale) lending, and various new credit services are accessible. These lending services can offer client-oriented services with conventional lending.
3. Wealth or PFM
Thanks to account aggregators, wealth and PFM (personal finance management) services have improved now. Client onboarding time has been tremendously lessened from several days to a few hours and, in some conditions, only a few minutes.
4. Neo-banks
The future of neo-banks looks promising. The most essential feature of their growth will be data exchange through AAs. They can assist Neo-banks in onboarding customers more effectively, connecting banking information, amending financial details in real-time, and having a centralised system to deal with all finances.
5. Account Reconciliation
AAs can access bank statements in real-time, making account reconciliation, supplier accounts, and customer liabilities much easier. Organisations can use AA-associated accounts to verify in real-time whether a particular transaction is reflected in the bank statement.
Generative AI for Financial Services
Generative AI is a class of algorithms that can develop the latest content, for example, text, images, or other types of info that closely relate to human-produced content. Not like conventional AI systems that depend on predetermined rules, GenAI uses highly developed machine learning algorithms, for example, deep neural networks, to understand patterns from big datasets.
Role of Generative AI for Financial Services
In the financial industry, GenAI is employed for activities like data study, risk and fraud identification, and improving customer experiences using personalised solutions. Its adaptability and ability to copy human-like creativity make it a changing power throughout industries. In significance, the applicability of Generative AI for financial services remains in its ability to enhance human abilities, centralise operations, and support an innovative culture.
Use Cases of Generative AI
1. Financial Summary: It can automate financial summaries. Based on historical data studies, genAI algorithms can produce precise and complete reports, saving time and decreasing the possibility of human errors.
2. Market Analysis: It can also be an important tool for performing market analysis, as it can assess large amounts of market info, forecast market trends, check customer choices, and run competitor analysis. When utilised ardently, advisors and professionals can get a competitive threshold and make data-oriented decisions.
3. Earnings Study: Training models on past earnings reports permits GenAI algorithms to create insights and forecasts about prospective earnings. This can assist advisors in making effective investment decisions and checking potential market possibilities.
4. Finance Planning: One of GenAI’s most encouraging use cases is that it can help in finance planning by assessing financial details and producing definite forecasts. Using past information and market trends, these algorithms can provide a view of future financial forecasts. This can help experts create successful strategies and amend resource sharing.
5. Performance Management: By assessing the performance details of financial products or portfolios, GenAI algorithms can create insights and suggestions for upgrading performance. This can help advisors in tracking and enhancing the performance of investments.
6. Risk Assessment and Management: Unapprehended by most experts, genAI can play a significant role in risk management. A model’s training details can prepare algorithms to produce risk models and check prospective risks, assisting professionals in checking and alleviating risks, enhancing decision-making, and assuring operational stability.
In Summary
With the above information, we can conclude that the RBI account aggregator allows people to safely share their financial details with fintech advisors, and generative AI can be employed to assess these details and give personalised guidance. If you are also considering an AA system to implement in your organisation to ease your financial operations and the lives of your customers, then look no further than Anumati. Anumati, as a provider of front-line technology and protected monetary solutions, authorises users to flawlessly consolidate their account data, acquire actionable vision, and make wise decisions.