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Finance

07
Jun
Pengenalan Talkbot  ·  Teknologi Voice AI
Mengoptimalkan potensi LLMs : era baru dari layanan keuangan
Jennifer Zhang

Jennifer Zhang

CEO & Co-Founder

Language Model Models (LLMs) adalah hal yang tengah menjadi perbincangan pada industry teknologi. LLM yang paling popular saat ini – OpenAI’s Chat GPT- telah terbukti menjadi perangkat yang sangat berguna untuk tugas sehari- hari, mulai dari merangkum teks yang sangat banyak hingga menulis email. Sudah tidak menjadi kejutan bahwa karyawan yang melek teknologi telah mulai menggunakannya untuk meningkatkan efisiensi mereka di tempat kerja. Tetapi menggunakan LLM pihak ketiga memiliki resiko dan keterbatasan.

Akhir-akhir ini, Bloomberg, perangkat lunak keuangan, data dan juga perusahaan media, telah meluncurkan LLM mereka sendiri yang disebut BloombergGPT. Model ini didesain secara khusus untuk para trader keuangan dan investor, dan menandai pencapaian yang signifikan dalam penggunaan LLM di jasa keuangan. Kita akan secara singkat mendiskusikan kemampuan dari BloombergGPT dan implikasinya pada industry keuangan.

 

Membantu Trader Untuk Memahami Berita

BloombergGPT didesain secara spesifik untuk membantu para trader dan investor untuk menganalisa data dan berita, sehingga memampukan mereka untuk membuat keputusan investasi yang lebih baik. Salah satu contohnya adalah BloombergGPT dapat mendeteksi apakah suatu headline berita mengindikasikan kenaikan atau penurunan harga saham. LLM yang didesain spesifik dalam bidang keuangan, menganalisa berita, laporan keuangan, social media feeds, dan sumber informasi lainnya untuk mengidentifikasi trend dan pola yang relevan.

Dan mirip seperti Bing Chat, BloombergGPT dapat menjawab pertanyaan seperti “Siapakah CEO dari Citigroup Inc?” dengan cukup akurat. Hal itu karena BloombergGPT telah dilatih menggunakan data hak milik (proprietary data) selama 40 tahun yang memampukan LLM untuk mengidentifikasi perusahaan, entitas bisnis, bahkan symbol saham (stock ticker symbols). Bloomberg akan mengintegrasikan LLM nya ke dalam salah satu dari layanan utamanya, Bloomberg Terminal, memampukan pengguna untuk mendapatkan nilai yang lebih dari database.

BloombergGPT adalah contoh yang kuat tentang bagaimana LLM (Large Language Models) dapat digunakan untuk mendukung industri jasa keuangan, dan potensinya baru saja ditemukan.

 

Merevolusi Layanan Keuangan

Contoh BloombergGPT ini menunjukkan bahwa LLMs memiliki potensi untuk mentransformasi industry layanan jasa keuangan dengan mengotomasi dan menyederhanakan berbagai proses. LLMs yang telah dikondisikan secara spesifik untuk bidang keuangan dapat menganalisa jumlah data yang besar, mengidentifikasi pola dan trend, dan membuat prediksi dengan akurasi yang tinggi.

Salah satu cara lain LLMs dapat digunakan pada jasa keuangan adalah pada fraud detection. Telah dilatih dengan menggunakan data historis dan pola beberapa transaksi, LLMs dapat digunakan untuk mengidentifikasi kemungkinan fraud dengan mendeteksi aktivitas yang tidak biasa. Aturan dapat dibuat bagi asisten AI untuk memberitahu institusi keuangan ketika muncul fraud, memampukan bank atau badan investasi untuk mengambil tindakan segera seperti memblokir transaksi atau akun untuk mencegah kerugian yang lebih besar.

Use case yang lebih umum yang sudah menggunakan general LLMs adalah penggunaan AI bots pada customer service. Dengan penggunaan general LLMs sudah dapat meningkatkan efisiensi customer service, memiliki LLM yang telah dikondisikan secara spesifik untuk bidang keuangan dapat meningkatkannya lebih lagi. LLM yang fokus pada industry keuangan dapat memproses pertanyaan customer yang secara spesifik berkaitan dengan keuangan dan menyediakan jawaban yang lebih baik. Dengan LLMs yang telah dikondisikan secara spesifik untuk bidang keuangan, AI bots dapat menangani pertanyaan-pertanyaan yang lebih kompleks, sehingga dapat lebih jauh mengurangi beban kerja dari agen customer service dan meningkatkan waktu respon.

 

Masa Depan Perbankan dan Investasi

Ada beberapa cara lain untuk menggunakan LLMs untuk meningkatkan efisiensi dan menyederhanakan operasi dalam layanan keuangan. Ini dapat digunakan untuk penerimaan karyawan, memberikan informasi yang penting pada karyawan baru. Pada industry yang sangat diatur seperti perbankan dan asuransi, LLMs berdomain khusus dapat digunakan untuk menganalisa dokumen regulasi dan mengidentifikasi bagian yang relevan yang memerlukan Tindakan untuk kepatuhan (compliance).

Dan meskipun BloombergGPT mengesankan, namun hanya berfungsi dalam satu bahasa – bahasa Inggris. Hal ini membatasi aplikasinya pada pasar dan wilayah yang lebih luas. Masa depan LLM dalam layanan keuangan adalah model multi-bahasa yang berdomain khusus yang berkinerja baik dalam tugas-tugas keuangan yang sempit maupun pertanyaan umum.

LLM keuangan bukanlah hal baru – sebenarnya, sebuah makalah penelitian tentang LLM keuangan telah diterbitkan 6 bulan sebelum pengumuman BloombergGPT. Namun, model kecerdasan buatan berdomain khusus ini memiliki potensi untuk merevolusi sector layanan keuangan. Mereka dapat membantu otomatisasi dan menyederhanakan proses, meningkatkan akurasi dan efisiensi, serta mengurangi risiko kesalahan manusia. Institusi keuangan yang mengadopsi teknologi LLM kemungkinan besar akan mendapatkan keunggulan kompetitif dibandingkan dengan pesaing mereka.

Ingin tetap menjadi yang terdepan dalam industri keuangan dengan LLM Anda sendiri? Bicaralah dengan salah satu ahli kami hari ini. 
Jelajahi LLM

09
May
Talkbot Basics  ·  Voice AI Technology
Unleashing the potential of LLMs: a new era for financial services
Jennifer Zhang

Jennifer Zhang

CEO & Co-Founder

Language Model Models (LLMs) are the latest buzzword of the tech industry, and for good reason. The most popular LLM today – OpenAI’s ChatGPT – has proven to be a powerful tool for day-to-day tasks, from summarising huge chunks of text to drafting emails. It’s no surprise that tech-savvy employees have started to use it to boost their efficiency at work. But using third-party general LLMs have risks and limitations. 

Recently, Bloomberg, the financial software, data, and media company, has launched its own LLM called BloombergGPT. The model is designed specifically for financial traders and investors, and marks a significant milestone in the use of LLMs in financial services. We’ll briefly discuss BloombergGPT’s capabilities and its implications on the financial industry moving forward.

Helping traders make sense of the news

BloombergGPT is specifically designed to help traders and investors analyze news and data, enabling them to make better investment decisions. BloombergGPT is able to detect whether news headlines indicate a bullish or bearish sentiment,  for example. The finance-specific LLM analyzes news stories, earnings reports, social media feeds, and other sources of information to identify relevant trends and patterns.

And similar to Bing Chat, BloombergGPT can answer questions like “Who is the CEO of Citigroup Inc?” – and do it fairly accurately. That’s because BloombergGPT is trained on 40 years of proprietary data that enables the LLM to identify corporations, business entities, and even stock ticker symbols. Bloomberg will be integrating its LLM into one of its core services, the Bloomberg Terminal, enabling subscribers to get even more value out of the search database.

BloombergGPT is a powerful example of how LLMs can be used to power the financial services industry, and its potential is only being discovered.

Revolutionising financial services

The example of BloombergGPT shows that LLMs have the potential to transform the financial services industry by automating and streamlining many processes. Finance-specific LLMs can analyze large amounts of data, identify patterns and trends, and make predictions with a high degree of accuracy.

Another way LLMs can be used in financial services is in fraud detection. Trained on historical data and typical transaction patterns,  LLMs can be used to identify potential fraud by detecting unusual activity. Rules can be set up for AI assistants to alert financial institutions when this occurs, enabling banks or investment houses to take immediate action like blocking transactions or accounts to prevent further damage.

A more common use case which is already being down with general purpose LLMs are AI bots in customer service. And while general LLMs already improve customer service efficiency, having a finance-specific LLM can improve outcomes even further. An industry-focused LLM can process specific finance-related customer inquiries and provide more fine-tuned responses. With finance-specific LLMs, AI bots may be able to handle more complex queries, further reducing the workload for customer service representatives and improving response times.

The future of banking and investment

There are various other ways to use custom LLMs to boost efficiency and streamline operations in finical services. It can be deployed for employee onboarding, providing essential information for new staff. In a highly regulated industry such as banking and insurance, domain-specific LLMs can be used to analyze regulatory documents and identify relevant sections that require action for compliance.

And while BloombergGPT is impressive, it only functions in one language – English. This limits its application into broader markets and geographies. The future of LLMs in financial services are multi-lingual, domain-specific models that perform well in narrow finance-related tasks and also general queries.

Finance LLMs are not new – in fact, a research paper on financial LLMs was already published six months before BloombergGPT was announced. Still, these domain-specific AI models have the potential to revolutionize the financial services sector. They can help automate and streamline  processes, improve accuracy and efficiency, and reduce the risk of human error. Financial institutions that embrace LLM technology will  likely gain a competitive advantage over their peers.

Want to stay ahead in the financial industry with your own LLM? Speak to one of our specialists today.
Explore LLMs

19
Jan
Talkbot Basics  ·  Voice AI Technology
AI technology in finance: from concept to implementation
Jennifer Zhang

Jennifer Zhang

CEO & Co-Founder

In a world of increasing data volume, managing spreadsheets and numbers is becoming more and more complex. Today’s finance organization needs a new set of tools to keep up with this fast-paced development. Enter AI technology. 

In this blog post, we present insights taken from Gartner’s webinar on the AI-Forward Finance Organization

Overload of financial data

Analysts predict that data volume will double to 181 zettabytes by 2025, which is a staggering amount. But what makes going through the world’s trove of digital data more challenging is its complexity, rather than its volume. Gartner foresees that data complexity will increase 4x in three years, twice the increase in volume. At the end of the day, it’s not just how much data we will get – it’s how much harder it will be to make sense of it all.

Many organizations today rely on spreadsheets for its operations, including financial functions. While these tools were powerful in their time, spreadsheets – even macro-enabled ones – are continuously being strained by high data volumes and complex data interconnectivities. It’s not uncommon to find broken links within spreadsheet models, or calculations that fail to present expected results due to technological limitations. The increase in data volume and complexity needs a smarter, more flexible solution.

Artificial Intelligence in finance

The rise of artificial intelligence (AI) in recent years has led to solutions that answer the data accessibility and navigability question. Enterprises are recognizing the power of AI and as of 2022, 59% of organizations surveyed by Gartner Research have started an AI initiative or adopted AI into their processes. And while this is a promising sign, it also speaks of risk to AI laggards. Businesses that fail to embrace AI technology to streamline processes will fall behind even faster in today’s accelerated market.

To embark on an AI journey, the key thing is to embrace change – to think differently and learn new things.

How to begin your AI journey as a finance organization

Start top-down – digital transformation, especially for finance-related activities, gets stronger buy-in when top management initiates the change
Understand that AI deployment is non-linear – adopt an agile working mindset and be willing to have more cyclical deployment patterns
Be open to experimentation – most organizations adopting AI take five iterations to get right things right or succeed

How to pitch a pivot to AI

Many leaders see the value of AI but need to get buy-in from upper management or other stakeholders. Here are some points that can help bring people into the fold and accelerate your company’s AI adoption.

Take the quick adoption route by buying software or subscribing to services. 
Position it as a proof-of-concept exercise where further adoption is dependent on results gained. Taking a packaged solution is one of the most frictionless ways for finance companies to dip their feet into the AI pond. For organizations that require debt collection or payment reminders, WIZ Talkbots are an easy way to introduce AI technology and quickly reap the benefits of AI-powered automation

Optimize human–machine collaboration.
Some of the pushback when it comes to AI stems from the fear of losing jobs. To overcome this, AI advocates should find ways where AI will complement human efforts. Best examples are process automations where humans are still needed for exception handling or complex cases. Remember, Humans are great at strategy and handling exceptions to rules, as well as seeing the big picture and drawing insights. Machines or AI are great at calculating, analyzing, executing processes, sending warnings at critical points, and enforcing rules or guidelines. Find the right balance between human and machine, and communicate how AI can empower humans to do more in less time or with less cost.

30% of businesses with advanced AI adoption report seeing better results than expected
– Faster implementation for new projects
– Greater business impact
– More process efficiencies
Significant AI adoption increased likelihood of financial benefit by 5x

Whether you’re a cutting-edge fintech or a well-established traditional, AI solutions should be in your future-proofing arsenal. And adopting a pre-built solution is one of the easiest ways to get started on your AI journey.

Looking to quickly deploy an AI solution into your debt collection operations? Our consultants would be happy to help
Contact us

29
Dec
Talkbot Basics  ·  Voice AI Technology
How AI can boost results in debt collection
Jennifer Zhang

Jennifer Zhang

CEO & Co-Founder

For many companies that need to conduct payment collections, resolving accounts that are past due is one of the trickiest parts of the business. Many of these customers are hard to contact, and once contacted, getting commitment to settle dues is another hurdle. Fortunately, conversational voice AI can help.

Harnessing the power of AI technology for debt collection

Artificial intelligence can be used in the debt collection process in a number of ways. First, AI can be used to automate manual processes like transcribing calls and categorizing customers based on their willingness to pay. These two tasks alone provide companies greater control over their debt collection processes because of the wealth of data that gets collected. Automatic transcription of calls transforms unstructured voice data into structured data. With it, enterprises can now do more detailed data analytics of their customer interactions. Those tasked with debt collection can gain a better understanding of risk behavior and improve collection tactics, moving from a reactive position into a more proactive one.

Loan provider gets more payment commitments with voice AI calls

This example features a finance company that provides loans and similar products to consumers. One of its biggest challenges is reaching accounts that are past due and making sure that commitment to payments are made. This often requires persistent outreach and multiple calls, which take a lot of time and effort for human agents. By switching to an AI-powered agent, the company was able to ramp up its call intensity proportionally to the account’s days past due.

call intensity 1-day, 2-day, and 3-day past due customers

With artificial intelligence, the company was able to call customers with overdue payments more consistently and with greater frequency. This resulted in obtaining “promise to pay” commitments from more customers – up to 60% for accounts that are three days past due.

Traditional bank gets clearer picture of consumer credit portfolio

In this second example, a large regional bank decided to deploy voice Talkbots for their credit collection operations. With each call, the AI engine records outcomes and tags customers accordingly.

The first layer of tags involve the call status – whether the customer was contacted or if the call failed to connect. Once the call connects and the Talkbot is able to speak with the customer, the AI engine then identifies whether or not the customer is willing to pay. If agreement is obtained, the AI agent then secures a payment date within a three-day window and records the response.

Having collections information structured in this manner enables the bank to see just how many of their delinquent accounts were willing to pay, and how soon. With this information, the bank is able to forecast its cashflows more accurately and update its collections strategy more proactively.

Fintech company gets record results in three-day collections campaign

This final example involves a non-traditional financial services provider that undertook a three-day call campaign for uncollected payments. Utilizing voice AI Talkbots, the fintech company was able to reach almost half of its customer database. Over 300,000 calls were made across a three-day period, obtaining payment commitments from two-thirds of customers that had overdue accounts. 

Of those that had committed to pay, over half promised to settle their dues within the day. Almost a fifth committed to pay the next day, bringing committed payments to 71% within a 48-hour window.

Getting ahead of back payments with artificial intelligence

AI powered much of the success of the above examples – artificial intelligence is what enabled companies to undertake the huge volume of calls to customers. And thanks to well-designed scripts, these companies were able to obtain payment commitments that previously eluded them. Results can be seen even in a short three-day campaign.
Are you looking to improve the outcomes of your debt collection activities? See how our voice AI Talkbots can help by booking a demo with us today.
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