Data analytics in financial services
How is data used in financial institutions?
Data is at the epicenter of financial services. We could even say that it is the vital flow that irrigates the entire profession. Data analysis ensures the smooth delivery of these flows and the integrity of the information conveyed. The three modules of the LOAMICS suite streamline the collection, storage, and processing of this vital information.
Data management is essential for financial institutions to have a single source of exactness. All teams can draw on it for valid information to create reports and dashboards that facilitate financial operations. This is one of the benefits of LOAMICS-DataLake and its integrated validity checking procedures.
Valuable banking capital markets data is used to help financial institutions measure day-to-day operations as well as to determine which investments will be most successful. This means that analytics data science can be used to manage the present and predict the future of financial services sector.
Enforcing data security and data governance to ensure safety of operations and to detect fraud
Complying with regulatory requirements like the Fundamental Review of the Trading Book
Freeing information from data silos with data integration tools for better decision making
Ensuring data quality to rely on a unique source of truth
Sales and marketing are the first to use financial data
The financial services sales and marketing industry operates at 3 levels of the customer lifecycle. They attract prospects through mostly digital campaigns and re-engage dormant customers with new products. Finally, they must ensure that they maintain a customer relationship that builds loyalty over time. These sectors can improve their acquisition efficiency thanks to Big Data, which enables banking financial services and others to deploy marketing campaigns with a high return on investment (ROI).
These campaigns are multi-channel in nature and allow for precise segmentation of prospect profiles, a relevant choice of marketing mix to be used, and advanced personalization of marketing messages. It is a question of monitoring the reputation that one has with customers and identifying those who are the most important and who have the most influence.
LOAMICS-AlgoEngine enables big data analytics professionals to create their own sources for complex and precise insights with big data technologies.
Improved risk management
A large part of IT activities of digitalized financial institutions like banks is to detect and manage risk. These risks can have the most devastating effects on financial operations. First, positive identification of customers and their transactions must be ensured. Fraud must be prevented and detected, and mechanisms linked to Big Data finance must be put in place to control access in real time.
Risk can also come from poor liquidity management. Both incoming and outgoing cash flow must be optimized to make financial operations as smooth as possible. Financial credit is another field of application of Big Data. It allows the creation of models based on transaction history and other criteria to reduce risk when granting loans. The same is true for the detection of credit card and insurance fraud. Finally, Big Data analytics can be used to protect against legal action resulting from poor file preparation.
With LOAMICS-AlgoEngine it is easy to develop unique data processing pipelines to fluidify legal and administrative processes with real time analytics.
The ability to consider frequent change in customer behavior and expectations
To face technological evolutions which are leading to larger amounts of data collected
To stay remain in competition of Fintech and banking players already using Big Data
To reduce regulatory pressures avoid fines and work more securely
To reduce operational costs and maintain a competitive advantage and secure a good profit margin
To benefit from technological evolutions enabling the processing of vast amounts of complex real-time data
Thanks to the automated procedures of Big Data, especially ETL, it is possible to acquire and store large volumes of data.
In addition to the data that summarizes online transactions, more and more data is acquired in an unstructured way. With the advent of NLP, it is possible to extract information from raw text to learn more about customers and partners. This wealth of information can be leveraged with machine learning algorithms to develop detailed views for certain industries.
Accurate KPIs can also be developed to provide a real-time view of the financial institution’s various activities. This can include both financial operations and those related to the operation of the organization itself.
The most effective marketing method for creating products that build customer loyalty is to personalize the marketing offers as much as possible. It is about understanding customer expectations and meeting them in ways that may seem insignificant at first glance.
Today’s marketing models are no longer focused on matching the business plans of organizations, but rather on meeting customer needs as closely as possible by offering quality products and services. Thanks to Big Data finance analysis, this new focus is easily achieved.
LOAMICS-ALgoEngine can probe LOAMICS-DataLake to extract customers personae from day-to-day transactions with data analytics financial.
By making the most of the large amounts of data available, financial institutions can invent innovative financial models.
There are many appropriate tools, some of which are automated, that can discern financial trends invisible to the naked eye.
Many neo-banks have grown rapidly by detecting behavioral changes in customers and incorporating them into their financial model.
Big Data can be a valuable lever for developing increasingly competitive financial models.
With data science, future trends can be predicted and anticipated, and new measurement tools can be invented. Short, medium and long-term visions that are outside the box are needed to disrupt the market.
These disruptive tools for analytics financial services can drive the development of new products creating new leaders in the industry.
Data analysis through Big Data framework provides the power of prediction in the markets to offer products that are more efficient and in line with current customer expectations.
Types of data analytics used in the financial services industry
The financial services industry is the most extensive user of data analysis techniques and tools. To analyze vast amounts of data on an ongoing basis as well as pre-determined dates such as the end of quarters or accounting periods. This data must be stored efficiently and be available permanently and instantaneously. Storage intelligencemust also include communication intelligence in the data interpretation. The entire pipeline of data analysis through Big Data that is used massively by financial services. With LOAMICS products, all types of analytics tasks can be performed without tedious and repetitive procedures.
- Study of customer behavior and expectations
- Responding to regulation and increasing cybersecurity
- Reduction in operational costs
- Implementing data analysis
Study of customer behavior and expectations
As interactions between financial institutions and their customers are increasingly based on digital exchanges, the collection of customer data is automated when accessing online services. In this case, the decrease in interaction between customers and financial services staff does not mean a decrease in engagement.
The same is true for social media, which is becoming increasingly important in exchange. Valuable insights can be gained from the social interactions between customers and their social networks. The customer experience can take place on several channels at the same time, provided that relevant data analysis methods are deployed.
One method is to deploy robotic advisors who are available 24/7 to provide services that were previously operated solely by humans.
It is totally possible to link LOAMICS Suite modules to external points of access like chatbots. Customers behaviors and expectations are then naturally deduced from any bot/user dialogue.
