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Banking and 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

22
Dec
Talkbot Basics  ·  Voice AI Technology
Spot and tag credit risks with voice AI technology
Jennifer Zhang

Jennifer Zhang

CEO & Co-Founder

For many financial institutions, consumer credit risk is the biggest risk on their balance sheets. This is underscored by the reality that credit risk is the biggest cause of bank failures, according to Van Greuning and Bratanovik of the World Bank.

To effectively manage credit risk – specifically consumer credit risk – companies in banking and finance need to accurately assess an individual’s ability to pay back a loan on time and in full. This means they need rich and accurate data to make the right judgements. But even with advances in technology and digital transformation, financial institutions still face challenges with data for credit risk management.

 

How AI technology is transforming risk management

Thankfully, artificial intelligence is changing how companies manage consumer credit risk. Voice AI technology is particularly helpful in unlocking data that has already been collected but just sits in storage. One prime example is customer call recordings, which are stored as voice data. While these recordings may be useful as reference, much of them sit as unstructured data that take time to sort and analyze. Recordings are usually not used or reviewed except for quality checks and investigations into specific incidents.

With AI technology, rich customer information can be extracted from this previously untapped resource – data that may be useful for credit risk assessments. Artificial intelligence enables call recordings to be quickly transcribed and converted into structured data, which can then be used for analytics. Within all that new data would be signals or keywords related to risk management.

 

Detecting intent with natural language understanding

Transcribing and structuring voice data is only the first step in making use of conversational AI capabilities. One of voice AI’s more powerful capabilities is intention detection – figuring out what a customer plans or wants to do, based on verbal cues. Questions like “How much do I owe again?” or “What’s the minimum amount due?” can signal an intention to settle the account soon. When the AI neural engine detects these words, it can automatically tag the caller as “willing to pay” and proceed to obtain a commitment date on payment.

Intention detection is useful for less common circumstances, too. Keywords like “family emergency” or “job loss” can signal personal distress, and the AI engine can then tag the call as “special case”. The call can be routed to a live human agent, who can then talk the customer through workable options to refinance their debt. This helps banks and financial institutions take a more humane approach towards their customers who might be going through a rough patch. It’s a win-win situation: customers feel cared for, and the finance company gets an early opportunity to rebalance its risk portfolio.

 

Using data analytics to inform strategy

Another key benefit of having calls tagged automatically by AI is the ability to notice patterns as they arise. For instance, if a significant number of calls have been tagged with “don’t know how to pay”, the bank can deploy an information campaign on payment modes. This reduces the risk of non-payment for future customers who may struggle with how to pay their bills online or offline.

What’s more, AI labels can be customized based on business patterns. Companies can choose any number of tags to attach to their voice data, using the most relevant information that can shape their decision-making. Tags can vary from committed payment dates – which help companies follow-up on payments – to payment modes, which inform the bank which channels are more effective for collections.

With more data in their hands, banks can also take a wider view of their risk portfolio and recalibrate their strategy in a more proactive manner. Tags such as “invalid number” or “uncontactable” can signal a bad debt, and keep companies from wasting time on chasing down a non-payer.

The use of smart tags and automatic labeling have already helped a number of fintech companies and traditional banks optimize their debt collections. And these are only some of the ways artificial intelligence can help streamline financial operations.

To find out other ways AI can improve your processes and build smarter customer engagements, speak to one of our specialists today.
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