AI coupled with machine learning development is proving to be a game changer for finance industries. Financial institutions like banks, non-banking financial institutions, even insurance and financial consultancy services benefit by the use of ML in their operations. Their systems become smarter with the passage of time as ML finds application in various areas.
Machine learning in financial sector
Machine learning is particularly helpful for finance industries that must cope with voluminous datasets covering various types of transactions and interactions. The traditional way would be for analysts to work on historical data and use manual methods to derive actionable insights. This is time-consuming, prone to errors and has built in obsolescence. Incorporate machine learning and the system becomes smarter since ML feeds on data, develops accuracy and delivers analytics for better decision making.
It does, however, require that ML developers have an understanding of how financial markets work and they must have access to large data sets to train the ML algorithm. The existing data infrastructure may require to updating. Developers can streamline these aspects and come up with tools that work to deliver positive outcomes through application in various areas.
Security and fraud detection
Security is a prime requirement for financial services providers and this is one area where machine learning development can bring in huge improvements. It is tough to handle hundreds of thousands of transactions, users and third parties involved in transactions with the result that security may be breached. ML can learn to recognize attempts at fraud or abnormal behavior and raise an alert in real time, not after the event.
Likewise, financial services can become a conduit for money laundering that would put the service provider in a legal tangle. Here too ML developers can train the system to spot any suspicious transactions and identify smurfing as an indicator of a laundering attempt.
Cyber threats, especially ransomware, pose a huge problem for security networks of financial services. Happily, ML algorithms can analyze and detect attempts at intrusion, injection or other forms of attack and raise an alert or be trained to simply thwart them. That payment services such as PayPal and Skrill are heavily into ML backed security is a pointer to just how important and useful it is.
Automation with precision
There are plenty of repetitive and routine tasks in the finance industry. ML can assist human workers or even replace them. The result is cost reduction along with improved productivity. Typical areas:
- Call centers can be automated to a high degree with intelligent IVR and chatbots that handle routine tasks such as answering queries, registering requests and even responding.
- Credit checks become easier as the ML powered system automates this task.
- Processing applications is another area where ML development results in more precise and speedy application processing. In addition, the system also identifies potential risks and prevents errors.
- Routine transactions become automated and securer, especially card transactions where suspicious activity results in an immediate alert or even blocking that transaction.
Trading
Trading in stocks gains from ML, especially day trades where traders must keep watch on movements of large numbers of scrips and take action. This is nearly impossible given that algorithmic trading is the norm these days. ML developers can power systems with algorithms that keep simultaneous track of thousands of scrips and automatically initiate a pre-ordered buy or sell instructions according to defined parameters. Speed is of the essence to grab an opportunity before prices change. Financial institutions and brokerage houses that implement algorithmic ML developed trading systems go one up in making profits rather than suffering losses on transactions.
Portfolio and fund management
This is another area of financial services where ML development brings more precision, automation and cost reductions to the table. The normal way is for an expert to handle portfolios and manage funds. With ML powered systems you have a machine system to keep track of each client’s portfolio, watch over market movements and take action to acquire or divest holdings.
Whereas human decisions may be prone to errors, ML based systems are infinitely more precise in integrating risks, desired goals, market movements, overall economic and political decisions in arriving at the right decision. Financial institutions benefit by way of more gains and better reputation. Their clients enjoy the benefit of reduced risk and better capital appreciation.
It is not just buy and sell decisions that ML takes care of. There may be issues such as failed trade settlements and predicting problematic trades that are all handled by ML based systems.
Underwriting, loans, mortgages
Underwriting is big business with potentially higher returns but also high risks if assessment is flawed. A first time applicant always faces more stringent scrutiny and decisions of human assessors may be subjective. ML bases its decisions on analyzing millions of past records and improves accurate approval rates.
There are people looking for student loans, home loans, business loans and mortgages. Manual scrutiny of documents and verification are time-consuming, error-prone processes. A worthy applicant may be rejected while another less-deserving may be approved in which case it leads to increase in non-performing assets. ML powered systems can assess documents and do it accurately as well as speedily. Banks, for instance, can match compliance standards, prevent fraud and disburse funds to the deserving applicants.
Sentiment analysis
This is an emerging area where ML makes its presence felt by helping finance industries tap into huge volumes of data such as chats, social media posts, audio, video and articles to find out what people think and expect. This can serve as inputs to improving existing products or coming up with products to meet existing and future demand.
Prep is essential
For ML to deliver results it must analyze millions of datasets and such data needs to be structured and organized. The first step in ML development for finance is to engage experts who can extract, clean, structure and transform data to be used for machine learning. Then the way forward is to engage ML developers to custom design solutions to meet any of the above application areas. The benefits are overarching:
- A system that becomes smarter with the passage of time
- Less dependence on human factor
- Precision and accuracy
- Speed of operations
- Cost reductions and superior customer experience
Conclusion
It does not happen in a day or a month. By its very nature ML requires to learn from voluminous data sets, be monitored and fine tuned to deliver precision results. You require ML developers and their team of AI developers, big data architects and scientists to work together. It is expensive but gains far outweigh the costs. Besides, if you do not have ML automation you are likely to be overtaken by competitors and eclipsed.
