-
Risk analysis management
Risk management is one of the most critical aspects of the provision of financial
services. This discipline is vital to the safety, reliability, and profitability of a
company’s day-to-day operations.
A variety of risks arises from the need to interact with competitors, investors,
regulators, customers, and other institutions. They differ in importance and the
potential for monetary losses. Accordingly, the first steps in reducing risks within
banks are identifying, prioritizing, and monitoring them.
By analyzing gigantic volumes of information on customers, loans, insurance results, and
other market operations, modern algorithms based on machine learning and risk management
data science methodologies independently improve risk assessment models, gradually
increasing institutions’ responsiveness and profitability.
Results: Doosan experienced a 30% increase in customer engagement and a significant
improvement in marketing ROI. The data-driven approach provided valuable insights for
informed decision-making.
Key Achievements:
- Increased Customer Engagement: Leveraging data insights for targeted marketing.
- Improved ROI: Optimization of marketing campaigns based on predictive analytics.
- Data-Driven Decision Making: Shifting towards informed and strategic decision-making
processes.
-
Customer data management
By accumulating customer information, you can build a behavioral profile to determine the
most appropriate sales promotion methods further. Having comprehensive information about
the client and the history of the company’s interaction with them, experts can identify
significant trends and predict future behavior with a high degree of accuracy. Moreover,
data science helps to automate this process and free up valuable employees’ time to
solve more critical and creative tasks.
-
Fraud detection
Machine learning algorithms allow timely detection and suppression of fraudulent
operations related to bank cards, accounts, transactions, and so on. For example, you
can highlight new accounts from which suspiciously expensive purchases are made. Banks
are also implementing systems for monitoring abnormal transactions based on behavioral
profiles. For example, if a customer suddenly orders a transaction that does not fit
into their typical behavior, banking algorithms may request additional confirmation to
complete it.
-
Personalization
Data Science for banking creates opportunities for organizing effective customer
interaction through personalized marketing. By analyzing the array of accumulated
information, self-learning algorithms compose individualized offers that will be most
interesting for this particular consumer. In turn, the company benefits from expanding
the range of offered products and services, as well as increasing sales.
-
Customer base segmentation
Segmentation helps you to serve multiple customer groups better. Groups are distinguished
based on behavior and other principles, using logistic regression, clustering, etc.
-
Designation of customer lifetime value
The ability to assess customer lifetime value (CLV) at the very beginning of the
interaction with them allows companies to move from focusing on quarterly profits to the
customer relationship management strategy that has already demonstrated increased
long-term profitability. Dividing customers into groups based on their CLV allows you to
focus efforts on improving the quality of service and increasing overall staff
productivity properly.
-
Real-time analytics
High quality real-time predictive data analysis allows businesses that use its power to
track all changes in the market. Today, banks that do not skimp on the introduction of
innovations track transactions, changes in credit ratings, new legislative initiatives,
and thousands of other factors affecting market conditions and react almost instantly.
-
Monitoring customer feedback
Machine learning algorithms and techniques enable you to analyze customer experiences,
creating a foundation to improve the effectiveness of service strategies and product
offerings.
-
Automation of communication with clients and the expansion of communication channels
Mobile communications, social networks, e-mail, instant messengers – a successful company
must communicate with its customers through any channels convenient for them. This is
also true for financial institutions.
Chatbots, electronic assistants, expert systems – today, a wide variety of automated
means of interaction with the consumer allows you to relieve the company’s staff from
routine tasks and increase communication efficiency.
Like any other company, when introducing innovations, financial institutions solve a set
of internal tasks: collecting data, attracting specialists, building a technology stack,
identifying effective marketing solutions – preparing all those plans and methodologies
that will be implemented in the targeted business processes.
At the same time, it is difficult to say what will “play” in the case of a particular
bank, and what will not justify the costs. How not to fail? t is necessary to design the
processes initially in such a fashion so that it is possible to measure results clearly.
It is crucial to define cost savings or profit increases relative to what was before the
integration of new methods. Anyway, an economic model can be calculated in almost any
case.
On average, data science tools show results in a short time frame (if you do not take
into account direct infrastructure expenses). Analyze tools that can improve processes,
try them, test hypotheses to see if they work or not. After that, decide upon the
viability of launching the project.
