AI in "Banking & Insurance"
Banks and credit unions need to think of technology-based resolutions to banking industry challenges.
Business Challenges
The banking industry is undergoing a radical shift, driven by new competition from FinTechs, changing business models, mounting regulation and compliance pressures. The customer experience is at the forefront of the challenges facing the banking industry today. In many ways, traditional banks are not delivering the level of service that customers are demanding, especially when it comes to technology Regulations in the banking and financial services industry continue to escalate, requiring banks to spend a large part of their discretionary budget on compliance.
How can AI help?
AI techniques, applied to internal data managed by organizations, allow banks and insurers to improve in two directions. On the one hand, they help them reach their customers better, offering them a higher level of personalization, and increasing loyalty (consumption prediction, up-selling, cross-selling, prediction of customer churn, product customization, etc.). On the other hand, they help them improve their processes, to be more efficient and, consequently, to improve their results.
Use cases
Making Investment Predictions: The fact that Machine learning enabled technologies give advanced market insights allows the fund managers to identify specific market changes much earlier as compared to the traditional investment models. ML technologies to develop automated investment advisors, the disruption in the investment banking industry is quite evident.
Secure Transactions: AI/Machine Learning based algorithms are excellent at detecting transactional frauds by analyzing millions of data sets that may be missed by naked eye. These models are generally built on the client’s behavior on the internet and transaction history.
Risk Management: Using machine learning techniques, banks and financial institutions can significantly lower the risk levels by analyzing a massive volume of data sources. Unlike the traditional methods which are usually limited to essential information such as credit score, ML can analyze significant volumes of personal information to reduce their risk.
Customer Data Management: Integrating machine learning techniques to manage such large volumes of data can bring both process efficiency and the benefit of extracting real intelligence from data. AI and ML tools such as data analytics, data mining, and natural language processing, help to get valuable insights from data for better business profitability.
Process Automation: Machine Learning powered solutions allow finance companies to completely replace manual work by automating repetitive tasks through intelligent process automation for enhanced business productivity. Machine Learning technology can easily access the data, interpret behaviors, follow and recognize the patterns.
Why choose us?
Machine Learning today plays a crucial role in different aspects of the financial ecosystem from managing assets, assessing risks, providing investment advice, dealing with fraud in finance, document authentication and much more.
We at RadomeTech believe AI is crucial for every business entity. Thus we have a developed a tool which lets the user have the power of Artificial Intelligence and Machine Learning !