Empowering the Financial Institutions with Machine Learning - PowerPoint PPT Presentation

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Empowering the Financial Institutions with Machine Learning

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The finance sector has seen tremendous growth in the last few years with the adoption of Machine Learning algorithms. The main reason for such growth is the rise in affordable computing prowess for streamlining operations, optimizing portfolios, and underwriting loans. – PowerPoint PPT presentation

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Title: Empowering the Financial Institutions with Machine Learning


1
Empowering the Financial Institutions with
Machine Learning
2
Introduction
  • The finance sector has seen tremendous growth in
    the last few years with the adoption of Machine
    Learning algorithms. The main reason for such
    growth is the rise in affordable computing
    prowess for streamlining operations, optimizing
    portfolios, and underwriting loans. The financial
    segment is one of the most crucial and
    substantial parts of the economy that needs a
    healthy basement through digital platforms.

3
  • AI and ML together can provide manifold
    advantages in terms of analyzing customer
    behaviour for sanctioning personalized loans and
    determining creditworthiness. The power of
    machine learning development services and
    solutions is in its ability to do code
    modifications for quicker, efficient, and
    accurate decisions.

4
Key Areas Where ML can Put to Good Use in
Financial Institutions
5
  • Financial services can use the potential of ML
    and DL algorithms for extracting customer
    insights through Big Data. This data can help
    them in creating the right models to make
    intelligent decisions. Lets dive deep into the
    working of ML and DL solutions and how they are
    an excellent fit for the financial industry

6
Underwriting and Trading
1
7
  • Several insurance companies stick to ML-based
    technology to extract and leverage its
    advantages. By analyzing customer earlier
    activities and forecasting possible actions,
    organizations can avoid potential risks and
    enhance operational effectiveness. Big banks,
    publicly-traded insurance firms, and health
    insurance companies can always set up additional
    security by utilizing ML on millions of examples
    of consumer data, and financial lending.

8
  • Algorithmic trading can automate the trading
    process by executing trades according to
    predefined criteria set by the trader. It
    automatically buys or sells stock quantities when
    it achieves a specific level. ML turns such
    trading practices into intelligent trading by
    offering a new and diverse set of tools. ML and
    AI together can analyze past market behaviour and
    determine an optimal market strategy, to make
    trade predictions, and more.

9
Management and Forecasting
2
10
  • The ML solutions have the potential to gain
    real-time breaking info on relevant market trends
    and events from different sources. Such data can
    be sent to customers to notify them of possible
    risks or even prevent financial crimes. Apart
    from that, Robo-advisors help in calibrating a
    business portfolio to the goals and risk
    tolerance of users. As per changes in the users
    goals and real-time changes in the market, these
    advisors aim at finding the best fit for the
    users original purposes.

11
  • Forecasting is an essential science to master in
    financial institutions, and ML helps in achieving
    that. They use past information to predict future
    growth possibilities and analyze industry trends.
    Older and new industries can take immense
    advantage through ML by creating reliable models
    for faster growth rates. ML and DL can help them
    in accumulating enough knowledge and experience
    to be effective by picking up even the slightest
    data variations.

12
Prevention and Security
3
13
  • Machine learning and stream computing
    technologies are an excellent way to conquer the
    challenges of frauds and security in financial
    institutions. Machines can verify volumes of data
    like texts, images, videos, analyze a pattern,
    and quickly detect an anomaly with higher
    precision. Several financial sectors are
    increasingly shifting to pattern analogy study
    using ML to tackle fraudulent cases.

14
  • ML doesnt use only a single method and combines
    a varied range of supervised and unsupervised
    methods in one system in innovative and novel
    ways to bring efficiency. Machine learning
    usually combines human pattern recognition skills
    with automated data algorithms for fraud
    detection. These tools mainly consist of data
    collection, application of ML methods, integrated
    operations, white boxing, and continuous
    monitoring.

15
Marketing and Analysis
4
16
  • Marketing is another application of ML solutions
    for finance that benefits corporate finance. ML
    brings predictive analysis to marketing by
    analyzing past behaviours, web activity, mobile
    app usage, and response to previous ad campaigns.
    These algorithms can predict the efficiency of a
    marketing strategy by bringing advanced,
    predictive marketing capabilities.

17
  • As financial institutions choose ML solutions,
    these tools will be at the forefront of marketing
    strategies. ML can also be useful in
    understanding social media, news trends, and
    other data sources. The stock market moves in
    response to myriad human-related factors, and ML
    can enhance financial activity by discovering new
    trends and telling signals. ML solutions in
    finance can go way ahead of stock and commodity
    data and can do much more than studying ticker
    symbols.

18
Automation and Interpretation
5
19
  • Financial institutions have tremendous
    opportunities ahead of them as they shift from
    spreadsheets to cloud-based data storage. ML with
    Blockchain and smart contracts can automate
    back-end and front-end processes. Fintech
    companies want to maximize their operational
    efficiency by adding an ML algorithmic solution
    to their data processes. ML can also perform
    real-time audits of the institutions operations
    and make regulatory compliance a more
    straightforward process.

20
  • Recent advances in deep learning have transformed
    image recognition accuracy beyond human
    capabilities. It can also help in the
    interpretation of documents, data analysis, and
    proposing intelligent responses by using the
    predictive power of identifying issues that need
    attention even before they occur. The ability of
    ML systems to quickly scan and analyze legal and
    other documents helps financial institutions in
    addressing the compliance issues and combat fraud.

21
Content and User Experience
6
22
  • Financial institutes can utilize ML solutions for
    creating content that can become a disruptive
    reality in the coming years. Advances in Natural
    Language Processing (NLP) and ML have given a
    competitive edge for such institutions by
    providing machine-generated content. ML software
    can quickly write most of the repetitive written
    communication media like financial summaries,
    company profiles, and stock reports.

23
  • Moreover, AI Chatbots and virtual assistants with
    integrated additional self-learning ML features
    can cause a sensation by adapting themselves
    according to each customer. Customers always need
    accurate and relevant information to fix their
    problems, and combining ML with AI can help
    achieve that. These innovative solutions can
    process large quantities of data and exclude
    human errors. The financial segment is
    increasingly finding it as a beneficial process
    of automation, paving the way for its popularity.

24
Ending Notes
7
25
  • The value of machine learning in finance is
    pretty apparent, as many institutions keep
    investing in the latest innovations. Such
    investments provide them with many benefits that
    include reduced operational costs, increased
    revenues, increased customer loyalty, better
    compliance, and risk management.

26
  • As the growing demand for ML-driven businesses is
    accomplishing new heights, companies need to
    comply with the changes to avoid risk and stay
    ahead in the competition. 9series is a leading
    machine learning development company that
    provides self-learning solutions for combating
    fraud in finance, authenticating documents,
    trading on stock exchanges and gathering crucial
    information as per your model and requirements.
  • Article Source
  • www.9spl.com/blog/empowering-financial-institution
    s-machine-learning/

27
9series
Leading Website App Design Company

www.9spl.com
Sales 91 9879465478 Email sales_at_9spl.com
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